ORIGINAL ARTICLE Earth Systems and Environment https://doi.org/10.1007/s41748-025-00921-7 Tadesse Terefe Zeleke t.terefe@cgiar.org; tadesse.terefe@aau.edu.et 1 The International Center for Tropical Agriculture, P.O. Box 5689, Addis Ababa, Ethiopia 2 The Ethiopian Agricultural Transformation Institute (ATI), P.O. Box 708, Addis Ababa, Ethiopia 3 International Center for Tropical Agriculture (CIAT), P.O. Box 823-00621, Nairobi, Kenya 4 Institute of Geophysics, Space Science and Astronomy, Addis Ababa University, Addis Ababa, Ethiopia Abstract Abstract  This study provides a comprehensive assessment of climate characteristics, variability, and their impacts on maize production in Southern Ethiopia, integrating historical observations with future climate projections. Using advanced statisti- cal analyses, including Mann-Kendall trend tests and Rotated Empirical Orthogonal Functions (REOF), we identify signifi- cant warming trends in both minimum and maximum temperatures across all seasons, alongside spatially heterogeneous rainfall variability strongly influenced by ocean-atmosphere interactions such as ENSO and the Indian Ocean Dipole. Our analysis reveals that maize yields in key agricultural zones, Sidama, South Omo, and Wolayita are highly sensitive to sea- sonal rainfall and temperature fluctuations, particularly during critical phenological stages. Future climate projections under SSP1 and SSP5 scenarios indicate decreasing rainfall during the main maize growing season (MAM) coupled with rising temperatures, exacerbating water stress and threatening crop productivity. Conversely, increased rainfall during the short rains (SON) may offer opportunities but also pose risks from waterlogging and pest outbreaks. These findings underscore the urgent need for climate-smart agricultural strategies, including drought-resistant varieties, adjusted planting calendars, and enhanced water management, to safeguard food security and rural livelihoods amid evolving climate risks in Southern Ethiopia. Graphical Abstract This is designed as a logical progression from foundational context to applied agricultural implications. An integrated cli- mate–agriculture assessment for South Ethiopia, synthesizing long-term observations, advanced statistical diagnostics, and state-of-the-art climate model projections to unravel past trends, key variability drivers, and future risks. The first section (Fig. 1) maps the region’s geographic setting, elevation, soils, and agroecological zones; factors that dictate crop suitability and shape vulnerability to climate extremes. Baseline climatologies of rainfall (Fig. 2) and minimum/maximum tempera- ture (Fig. 3, supported by Fig. 3a) capture seasonal cycles and spatial gradients, forming essential references for detecting anomalies. Trend analyses (Fig. 4–5, supported by Fig. 4a, 5a–5d) uncover significant spatial–temporal changes in rainfall, temperature, and sea surface temperature (SST) between 1981–2024, including season-specific precipitation declines or increases and pervasive warming in both minimum and maximum temperatures. These shifts have direct implications for heat stress, evapotranspiration rates, and the potential redefinition of agroecological zones. Rotated Empirical Orthogonal Function (REOF) analysis (Fig. 6 and 8, supported by Fig. 8a–8c) isolates dominant rainfall variability modes, linked to large-scale ocean–atmosphere phenomena. Correlation and composite diagnostics (Fig. 7, supported by Fig. 7a–7c) empha- size the seasonal influence of SST anomalies, El Niño-Southern Oscillation (ENSO), and the Indian Ocean Dipole (IOD), Received: 27 April 2025 / Revised: 29 September 2025 / Accepted: 19 October 2025 © The Author(s) 2025 Advancing Spatiotemporal Analysis of Climate Variability: Current Trends, Future Projections, Causes, and Impacts on Maize Production in Southern Ethiopia Tadesse Terefe Zeleke1,4  · Sintayehu Alemayehu1,3 · Dagnachew Lule2 · Aweke M. Gelaw2 · Sintayehu W. Dejene1,4 · Lidya Tesfaye1 · Pedro A. Chilambe3 · Evan Girvetz3 1 3 https://doi.org/10.1007/s41748-025-00921-7 http://orcid.org/0000-0001-5189-8189 http://crossmark.crossref.org/dialog/?doi=10.1007/s41748-025-00921-7&domain=pdf&date_stamp=2025-11-19 T. T. Zeleke et al. 1  Introduction Ethiopia’s diverse topography and geographical features make vulnerable to climate variability, characterized by sig- nificant spatiotemporal fluctuations in both rainfall and tem- perature (Zeleke et al. 2013). These climatic characteristics clarifying connection pathways that govern rainfall variability. The climate–agriculture coupling (Fig. 9) demonstrates mea- surable impacts of seasonal rainfall anomalies on maize yield, production, and harvested area, highlighting farmers’ adaptive strategies under water stress. Multi-model ensemble analyses (Fig. 10–11; see also supplementary Figs. 10a–10c, 11a–11c) under two Shared Socioeconomic Pathways: SSP1 (Sustainability) and SSP5 (Fossil-Fueled Development), elucidate model uncertainties and indicate projected shifts in rainfall regimes and enhanced warming, thereby informing targeted adaptation strategies. Collectively, these findings advance scientific understanding of climate–agriculture interactions, enhance seasonal forecasting capability, and provide evidence-based guidance for policymakers, extension services, and climate-resilient agri- cultural investment in one of East Africa’s most climate-sensitive regions. Highlights ● Distinct seasonal and spatial climate variability in Southern Ethiopia strongly influences maize production dynamics. ● Rainfall variability is closely linked to global ocean-atmosphere drivers such as ENSO, Indian Ocean Dipole, and Atlan- tic SST anomalies. ● Maize yields are highly sensitive to seasonal rainfall and temperature changes, with critical growth stages most vulner- able to climate fluctuations. ● Future projections indicate reduced rainfall during the main growing season (MAM) and increased temperatures, posing significant risks to maize productivity. ● Adaptation strategies including drought-resistant crops, adjusted planting schedules, and improved water management are essential to enhance resilience and food security. Keywords  Climate variability · Global climate oscillations · Maize production · Climate projection · Agricultural resilience · South ethiopia 1 3 Advancing Spatiotemporal Analysis of Climate Variability: Current Trends, Future Projections, Causes, and… impact severely on the agriculture sector, especially in rural areas where rainfed farming is predominant (Sinore and Wang 2024). According to the Intergovernmental Panel on Climate Change (IPCC 2021), climate change will inten- sify these fluctuations, worsening challenges related to food security and economic stability across the country (Ayal et al. 2023). This is especially concerning in regions like Southern Ethiopia, where agriculture is highly dependent on climatic conditions, with maize being a key crop sensitive to these fluctuations (Alemayehu et al. 2024a, b; Haile et al. 2021). Rainfall variability is shaped by large-scale atmospheric and oceanic phenomena, including shifts in the Intertropical Convergence Zone (ITCZ), changes in wind patterns, and fluctuations in sea surface temperatures (SSTs) across the Indian and Atlantic Oceans, as well as the El Niño–Southern Oscillation (ENSO). These global factors, alongside local variables (Diro and Grimes 2011; Zeleke et al. 2017; Zeleke and Damtie 2017), influence broad-scale pressure systems and atmospheric dynamics, ultimately affecting rainfall distribution and the frequency of extreme weather events. The March to May (MAM)/June-August (JJA) season, often considered as the main rainy period in most/some part of southern Ethiopia, plays a vital role in supporting maize production (Fig. 1, Fig. 2) and September to November (SON) season serves as a secondary rainfall period. Climate variability during these times is influenced by the complex interplay of local, regional, and global climate systems (Luk- wasa et al. 2022; Zeleke et al. 2017, 2024). These climatic drivers affect both the onset and intensity of rainfall, thereby shaping crop growth and water resource availability (Haile et al. 2021; Lukwasa et al. 2022). Additionally, variations in the subtropical westerly jet stream and tropical cyclones originating from the Indian Ocean contribute to fluctuations in rainfall, making the timing of precipitation particularly critical for maize development (Hasan and Pattnaik 2024; Hunt et al. 2015; Zeleke and Damtie 2017). Despite this, the influence of climate variability on maize production, along with its underlying local and global drivers, remains insuf- ficiently documented in the region. Fig. 1  Visual synopsis of the study areas (south Ethiopia maize Commercialization Clusters, ACC) 1 3 T. T. Zeleke et al. shifts in rainfall timing or intensity can disrupt planting and harvesting schedules, increasing the risk of crop failure due to drought or flooding. Research indicates that delayed rain- fall onset or early cessation during the MAM season can impede crop establishment and maturation, while excessive rainfall may cause waterlogging, damaging maize roots and inhibiting growth (Habte et al. 2023; Omay et al. 2023). Maize plays a central role in ensuring food security and sustaining local economies in Southern Ethiopia, making it imperative to understand the drivers of rainfall variability. As climate change accelerates, the frequency and intensity of extreme weather events are expected to increase, posing significant risks to stable food production systems (Eren- stein et al. 2022; Sherry et al. 2020). In light of these chal- lenges, this study investigates the seasonal variability of rainfall and its impact on maize production, offering valu- able insights into adaptation strategies aimed at enhancing agricultural resilience. By analyzing the spatiotemporal variability of climate across multiple scales and examining long-term trends, the study evaluates how fluctuations in This study examines the climatic factors influencing Southern Ethiopia, with a particular focus on the Agricultural Commercialization Clusters (ACCs): strategic regions des- ignated for boosting productivity, market access, and agri- cultural transformation (Fig. 1). Within these ACCs, maize serves as a critical staple crop, and its productivity is closely linked to the timing, intensity, and variability of rainfall, as well as temperature fluctuations. Variability in rainfall, espe- cially in onset, frequency, and cessation: can profoundly affect crop performance, directly influencing food security and livelihoods in the region (Ayinu et al. 2022, 2023). Gaining a deeper understanding of these climatic dynamics is essential for enhancing agricultural resilience. The study also investigates the impact of climate variability on maize production in major maize-growing areas of Southern Ethi- opia, including Wolayita, Gofa, South Omo, and Sidama. Agricultural activities in these zones and regions are highly sensitive to seasonal climate patterns. Although irrigation has potential, its role remains underexplored and falls out- side the scope of this analysis. Consequently, even slight Fig. 2  Monthly rainfall climatology of maize ACC selected zones of south Ethiopia during 1981-2024 1 3 Advancing Spatiotemporal Analysis of Climate Variability: Current Trends, Future Projections, Causes, and… outlines the traditional agroecological zones, essential for identifying crop suitability and guiding sustainable land‐use planning. Panel (d) maps the spatial distribution of domi- nant soil types: Acrisols, Cambisols, Ferralsols, Fluvisols, Nitisols, Andosols, Vertisols, Xerosols, and Yermosols, providing critical context for evaluating land productivity and crop responses to climatic variability. These physical characteristics form the biophysical baseline for climate impact assessment and the development of targeted adapta- tion strategies. Comprehensive site‐specific descriptions are available in Alemayehu et al. (2024a). 2.2  Data Source This study employs a comprehensive suite of high-resolu- tion climate datasets to evaluate rainfall variability and its influence on agricultural productivity in Southern Ethio- pia. The primary source of precipitation data is the Cli- mate Hazards Group Infrared Precipitation with Station (CHIRPS v3), developed by the University of California, Santa Barbara. CHIRPS offers high-resolution precipita- tion data at 0.05° x 0.05° (approximately 5.3 km) and spans from 1981 to the present (Funk et al. 2015). Temperature data were obtained from the Climatic Research Unit Time Series (CRU TS v4.08), a globally recognized dataset that provides gridded temperature records at a spatial resolution of 0.5° (Harris et al. 2020). To enhance local accuracy, the CRU data was further downscaled and interpolated using high-resolution Digital Elevation Model (DEM) informa- tion, following established approaches for improving cli- mate data representation in complex topographies (Dinku et al. 2014; Dinku 2019). To assess global climate influences, the study integrates the Hadley Centre’s Global Sea Ice and Sea Surface Temperature dataset (HadISST), which sup- ports analysis of large-scale ocean-atmosphere interactions (Rayner et al. 2003). Topographical and land use patterns are captured using DEM and the United States Geological Survey (USGS), offering critical insights into the spatial distribution of agroclimatic zones and their exposure to cli- matic stressors (Fig. 1). Local agricultural data, including crop yields and cultivated areas, were obtained from the Ethiopian Statistical Service (ESS), ensuring a localized perspective on the impacts of climate variability. Future climate projections are derived from the Coupled Model Intercomparison Project Phase 6 (CMIP6), accessed via the International Center for Theoretical Physics (ICTP). The projections are dynamically downscaled using RegCM and incorporate simulations from HadGEM3, MPI- ESM1.2, NorESM2 and ensemble models. These models were selected because they are among the few downscaled products currently available for East Africa, and previous evaluations have shown that they capture regional climate rainfall and temperature influence maize yields. It further incorporates future climate projections, using downscaled General Circulation Models (GCMs) with Fifth Generation Regional Climate Modeling System (RegCM5) to assess potential scenarios. The ultimate objective is to inform the development of effective adaptation strategies that can strengthen the resil- ience of agricultural systems and safeguard food security in the face of a changing and increasingly unpredictable cli- mate. In doing so, the study provides critical evidence for policymakers and stakeholders to guide targeted interven- tions that mitigate the adverse effects of climate variability on maize production in Southern Ethiopia. 2  Methodology 2.1  Study Area This study focuses on selected maize Agricultural Com- mercialization Clusters (ACCs) designated by the Ethiopian Agricultural Transformation Institute (ATI). The selection was guided by multiple criteria, including population den- sity, historical maize production trends, data availability, logistical accessibility, and the representativeness of South Ethiopia’s major agroecological zones (Alemayehu et al. 2024a; Fig. 1). Applying these criteria, the study concen- trated on clusters that provide both accessibility and reli- able data coverage. Accordingly, the research targets three administrative zones: Wolayita, South Omo, and Gofa, along with the Sidama region. These areas are among Ethiopia’s key maize-producing zones and are particularly sensitive to seasonal rainfall variability, making them suit- able for analyzing the impacts of climate variability on maize productivity. Rainfall seasonality is analyzed using both the Northern Hemisphere classification (DJF, MAM, JJA, SON) to capture large-scale climatic drivers, and the Ethiopian National Meteorological Institute’s classification (FMAM, JJAS, ONDJ) to align with national sectoral appli- cations. Particular attention is given to the March–Septem- ber agricultural growing season (Fig. 2), which underpins food security and rural livelihoods. Given this dependence, rainfall fluctuations directly influence maize yields, high- lighting the critical need to understand precipitation dynam- ics in the region. Fig. 1a shows the geographic position of the study area in relation to major water bodies and elevation gradients, highlighting its alignment along the Great Rift Valley and proximity to adjacent oceanic systems, both key drivers of regional climate. Panel (b) presents an elevation map of Ethiopia, illustrating the complex topography that shapes local climate conditions and agricultural potential. Panel (c) 1 3 T. T. Zeleke et al. To explore linkages between global climate-drivers and local rainfall, multiple correlation techniques were employed. Pearson’s correlation measured linear associa- tions between Sea Surface Temperature (SST) and rainfall, while Kendall’s tau and Spearman’s rank correlation cap- tured monotonic and non-linear relationships, respectively (Kendall 1975; Wilks 2011). To detect more complex, non-linear dependencies, distance correlation was applied with composite analysis dry/wet years with correspond- ing global/local mechanisms, offering a more sensitive approach to identifying relationships that former methods might overlook (Székely et al. 2007). Future climate impacts were assessed using a bias analy- sis method, comparing the historical mean (1990–2020) with the projected mean (2020–2050) for rainfall and tem- perature under SSP1 and SSP5 scenarios (Hawkins et al. 2013). This helped identify projected anomalies and shifts from the baseline, offering insights into potential future cli- mate dynamics. Additionally, standardized anomalies were calculated for seasonal areal mean time series of rainfall and temperature, capturing year-to-year deviations and enhanc- ing the understanding of both historical and future vari- ability. Together, this integrative methodological approach provides a comprehensive and nuanced analysis of climate variability and its implications for maize productivity in Southern Ethiopia, laying the groundwork for informed adaptation planning and resilience-building strategies. 3  Results and Discussion 3.1  Spatiotemporal Analysis of Climate Variables To characterize the climatic context of the study area, we examined long‐term (1981–2024) rainfall, minimum tem- perature, and maximum temperature patterns across Gofa, Wolayita, South Omo, and Sidama. These variables were selected for their direct influence on crop growth, water availability, and agricultural decision‐making. Seasonal and spatial climatologies were analyzed to identify base- line conditions, detect distinct regimes, and quantify the magnitude and timing of key climatic phases. Zonal annual cycles complement the spatial maps by highlighting intra‐ annual variability and pinpointing periods of climatic stress or opportunity for agriculture. Establishing these baselines is critical for detecting anomalies, assessing interannual variability, and interpreting the potential impacts of cli- mate change on maize‐based farming systems in Southern Ethiopia. The analysis of seasonal rainfall across the Gofa, Wolay- ita, and South Omo zones of Southern Ethiopia, along with the Sidama region, reveals marked spatiotemporal features reasonably well (IPCC 2021; O'Neill et al. 2016). The analysis covers near-surface air temperature (maxi- mum, and minimum) and precipitation for both historical (1990–2020) and projected (2020–2050) periods. The pro- jection horizon was limited to 2020–2050, reflecting both the availability of high-quality downscaled data and the pol- icy relevance of near- to mid-term futures, which are most critical for adaptation planning in Ethiopia. Two Shared Socioeconomic Pathways (SSPs) guide the future scenario analysis: SSP1, reflecting a low-emissions pathway, and SSP5, representing a high-emissions trajectory: analogous to RCP2.6 and RCP8.5 from the CMIP5 framework (IPCC et al. 2021; O’Neill et al. 2016). By integrating historical records, future projections, and detailed environmental data, this study delivers a robust, multi-dimensional assessment of climate variability and its potential effects on maize pro- duction across Southern Ethiopia. 2.3  Statistical Methods To complement the robust data framework, this study applies a suite of advanced statistical techniques to ana- lyze spatiotemporal climate variability, investigate causal relationships, and evaluate future climate change impacts on agriculture. These methods are specifically chosen to handle the inherent complexities of climate data, such as non-normal distributions, outliers, and non-linear associa- tions ensuring reliable and interpretable outcomes. To detect and quantify trends in climatic variables, the non-parametric Mann-Kendall test was employed, which is well-suited for identifying monotonic trends in time series even in the pres- ence of outliers or skewed data (Kendall 1975; Weldegerima et al. 2018). The Sen’s Slope Estimator complemented this by providing robust estimates of the magnitude of change, particularly for variables like temperature and precipitation (Sen 1968; Weldegerima et al. 2023). To capture dominant patterns of climate variability across space and time, Rotated Empirical Orthogonal Func- tion (REOF) analysis was conducted. This method enhances traditional Empirical Orthogonal Function (EOF) results by rotating spatial patterns and their corresponding tempo- ral components, known as Rotated Principal Components (RPCs), making the identification of regional climate sig- nals more intuitive and meaningful (Navarra and Simoncini 2010; Richman 1986). In addition, the coefficient of varia- tion (CV), expressed as a percentage, was used to assess relative variability in both historical and projected datasets, allowing for comparisons across different periods and vari- ables, including temperature and rainfall under the SSP1 and SSP5 scenarios (Wilks 2011). This enabled a clear eval- uation of the consistency and stability of climate conditions across time. 1 3 Advancing Spatiotemporal Analysis of Climate Variability: Current Trends, Future Projections, Causes, and… the complexity of seasonal climate dynamics in Southern Ethiopia. Recognizing these patterns is critical for opti- mizing planting schedules, managing water resources, and mitigating the impacts of climate variability on agricultural livelihoods. Figure 3 depicts the long‐term spatial and temporal distri- bution of minimum temperature across the study area from 1981 to 2024, providing essential climatological context for agricultural and climate impact assessments. Panels 3a–3l show the monthly mean minimum temperature, illustrat- ing the seasonal progression of cooler and warmer condi- tions, along with a pronounced gradient, declining from the southwestern lowlands of South Omo (~22°C) to the cen- tral highlands of Sidama (~6°C). These patterns underscore significant spatial contrasts in night‐time temperatures and seasonal transitions, which are critical for evaluating crop sensitivity, frost risk, and evapotranspiration dynamics. Topography exerts a dominant influence on these tempera- ture variations, reflecting the complex relationship between elevation and thermal regimes (Ogunrinde et al. 2024). Fig. 3m summarizes the annual cycle of zonal mean minimum temperature, condensing monthly values into smooth curves that capture the onset, peak, and retreat of cooler periods in each zone. This enables the identification of thermal thresh- olds and critical windows for planting, germination, and crop growth. Minimum temperature is a key determinant of agroecological suitability, shaping plant physiology, pest and disease dynamics, and overall crop resilience. Estab- lishing this baseline is essential before examining anoma- lies, trends, or variability, and it serves as both a reference for typical night‐time conditions and a diagnostic tool for detecting deviations from expected thermal patterns. Such understanding is fundamental for interpreting climate‐ related agricultural risks, guiding adaptation strategies, and supporting long‐term planning. Maximum temperature patterns across the study area (1981–2024) reveal clear spatial gradients, with hotspots in South Omo (~39°C) and moderated values in the higher‐ elevation zones of Gofa and Sidama (~15°C). Cooler condi- tions occur from June to September, linked to cloud cover and residual soil moisture from the MAM rains, while peaks in February–March reach >33 °C in South Omo and ~30 °C elsewhere. These seasonal dynamics, consistent with Degefu (1987) and Ogunrinde et al. (2024), reflect mini- mal cloud cover and greater solar radiation during the dry season. The zonal annual cycle, capturing seasonal peaks and troughs relevant for identifying heat‐stress periods that may disrupt flowering and reduce yields in temperature‐ sensitive crops. Maximum temperature strongly influences evapotranspiration, crop phenology, and livestock comfort. The pronounced elevation‐driven gradient, documented by Weldegerima et al. (2023), underscores topography’s role in variability in precipitation patterns (Fig. 2). Over the 44‐ year period (1981–2024), these variations provide essential climatological context for understanding rainfall regimes critical to agricultural productivity. Panels (a–l) in Fig. 2 present monthly long‐term mean rainfall, highlighting the spatial distribution of precipitation throughout the year and clearly distinguishing unimodal and bimodal regimes. Panel (m) synthesizes this information into annual rainfall cycles for each zone, capturing the timing, intensity, and duration of rainy seasons, as well as transitions between wet and dry periods. Establishing these baseline climatologies is vital for identifying anomalies, evaluating interannual variabil- ity, and assessing the impacts of rainfall fluctuations on crop performance, water resources, and agricultural planning (Alemayehu et al. 2024c). Distinct seasonal rainfall patterns are evident across the study areas, shaped primarily by the region’s bimodal rainfall system. The main (MAM/Spring) rains occur from March to May, followed by a shorter rainy season (Autumn) from September to November, except in the Wolayita Zone. In South Omo, rainfall is distinctly bimodal, with MAM contributing 362 mm (39.2% of annual total) and peaking in April (~145 mm, 16.7%), while SON delivers 279 mm (30.3%), peaking in October/November (~103 mm, 10.9%) (Fig. 2). These findings are consistent with Zeleke et al. (2013), who underscored the importance of seasonal rain- fall characterization for agricultural scheduling in rainfed systems. In contrast, Gofa and Sidama exhibit weaker bimodal pat- terns. In Gofa, the main rain (MAM) total 601 mm (36.8%), peaking in April (232 mm, 14.4%), while SON accounts for 461 mm (28.3%), with an October peak (180 mm, 11.1%). Sidama records 427 mm (35.3%) during MAM, peaking in May (168 mm, 14.1%), and 339 mm (28%) during SON, with September/October peaks (141 mm, 11.1%). These fragmented seasonal distributions contrast with South Omo’s well‐defined bimodal regime and align with Markos et al. (2023), who documented similar rainfall disparities across southern Ethiopia with implications for crop cycles and water management. Wolayita exhibits a more uniform rainfall distribution between March and October, with notable contributions from MAM (418 mm, 33.9%), JJA (431 mm, 34.9%), and September–October (244.1 mm, 19.8%). May is the wettest month (~164 mm, 13.5%). This pattern resembles a mono- modal‐like regime, offering more consistent rainfall through the core growing period. Spatial rainfall variability across Wolayita and the wider region is strongly influenced by elevation, as emphasized by Alemayehu et al. (2024b) and Enyew and Steeneveld (2014), who identified topography as a major driver of precipitation distribution in Ethiopia. The observed spatiotemporal rainfall heterogeneity reflects 1 3 T. T. Zeleke et al. Figure 4 presents a detailed analysis of long‐term rainfall trends across the study area from 1981 to 2024, employing the Mann-Kendall Trend Test and Sen’s Slope Estimator. Monthly spatial patterns of statistically significant rain- fall trends (P < 0.05) are shown in Fig. 4(a–l), revealing the temporal and geographic variability of increasing or decreasing precipitation on a month-by-month basis. Sea- sonal trends (Panels m–t) follow both the Northern Hemi- sphere climatological calendar (DJF, MAM, JJA, SON) and the Ethiopian Meteorological Institute’s seasonal classifica- tions (FMAM, JJAS, ONDJ), with particular focus on the critical agricultural growing season (March–September, Panel t). This analysis elucidates the direction, magnitude, and spatial extent of rainfall changes, providing essential insights for agricultural planning, water management, and climate adaptation. Identifying months and regions under- going significant shifts, it offers a strategic tool for climate- smart agriculture, enabling stakeholders to align planting calendars and risk management strategies with emerging climatic realities. Multiple statistical methods were applied shaping thermal regimes, with significant differences over short distances between lowlands and highlands. These pat- terns provide a baseline for detecting anomalies, assessing climate risks, and informing heat‐adaptation strategies in agriculture. 3.2  Climate Change and Variability 3.2.1  Climate Trends This subsection examines long-term trends in key climatic variables: rainfall, maximum temperature, and minimum temperature, across the study region from 1981 to 2024. Analyzing statistically significant changes over monthly and seasonal timescales, this assessment provides critical insights into evolving climate patterns that directly impact agricultural productivity and resource management. Under- standing these trends is essential for anticipating climate- related risks and informing adaptive strategies tailored to the region’s unique agroecological contexts. Fig. 3.  The long-term spatial and temporal patterns of minimum temperature across the study area over a period (1981–2024) 1 3 Advancing Spatiotemporal Analysis of Climate Variability: Current Trends, Future Projections, Causes, and… dynamics may be modulating regional precipitation, leading to increased moisture during SON. This enhanced rainfall during SON could benefit maize production, as this period constitutes the secondary cultivation season critical for crop growth in Southern Ethiopia. Increased water availability during SON may improve yields, especially in areas depen- dent on the minor rainy season for supplemental moisture. However, shifts in seasonal rainfall timing also carry risks of disrupting traditional agricultural calendars, necessitating to cross-validate results, enhancing confidence in distin- guishing genuine climate signals from noise. The study reveals a significant increase in rainfall dur- ing the SON (September–November) season across the study zones, while no substantial trends were observed dur- ing other seasons (Fig. 4). This amplification of the minor rainy season suggests a shifting rainfall distribution poten- tially driven by broader climatic factors, such as rising sea surface temperatures in the eastern Somali Sea and related atmospheric circulation changes. These ocean-atmosphere Fig. 4  A significant monthly and seasonal rainfall trend pattern (P-value < 0.05) was identified for the period 1981-2024 using the Mann-Kendall Trend Test and the Sen Slope Estimator 1 3 T. T. Zeleke et al. warming rates occur during January–February and June. This seasonal variation may be linked to regional ocean- atmosphere dynamics such as the positive Indian Ocean Dipole during SON (Wenegrat et al. 2022). Interestingly, traditionally cooler months (DJF) are warming at rates that surpass those in historically hotter seasons, signaling a shift in regional climate regimes. This accelerated warm- ing during the cool season raises concerns for agriculture and ecosystems adapted to lower temperatures (Wenegrat et al. 2022). Moreover, maximum temperature increases are adaptive responses from farmers to optimize productivity under evolving climatic conditions. A comprehensive analysis of statistically significant maximum and minimum temperature trends across the study region during a period 1981–2024 is shown in Fig. 5. The analysis reveals consistent and significant warming trends in both maximum and minimum temperatures across all seasons, with the most pronounced increases observed during the SON (September–November) season, particu- larly for minimum temperatures. In contrast, the lowest Fig. 5  A significant monthly and seasonal maximum temperature long-term change pattern (P-value < 0.05) was identified for the period 1981- 2024 using the Mann-Kendall Trend Test and the Sen Slope Estimator 1 3 Advancing Spatiotemporal Analysis of Climate Variability: Current Trends, Future Projections, Causes, and… boundary between Wolayita and Sidama, and the third mode highlights the Gofa area (Table 1). Similar spatial patterns emerge during JJA, emphasizing the persistence of these rainfall variability hotspots across the seasons. The tempo- ral evolution of these modes, represented by the Rainfall Principal Components (RPCs), captures the interannual fluctuations of rainfall intensity, with notable wet and dry years that reflect the dynamic nature of the region’s hydro- climate. These temporal profiles provide crucial insight into the timing and magnitude of seasonal rainfall variability, which are directly relevant for agricultural scheduling and water resource planning. To better understand the drivers behind these rainfall patterns, Fig. 7 presents the statistically significant Pear- son correlations between the dominant seasonal RPCs and global Sea Surface Temperature (SST) anomalies. During the MAM season, rainfall variability shows a strong posi- tive correlation with SSTs in the equatorial Indian Ocean, west of Senegal and Equatorial Guinea and the northern equatorial Atlantic Ocean. The warming/cooling of these oceanic regions influences the Intertropical Convergence Zone (ITCZ) position, which governs moisture transport and convective activity over southern Ethiopia. Warmer SSTs in the equatorial Indian Ocean intensify atmospheric convection and enhance moisture availability, consistent with findings by Saji et al. (1999) and Nicholson (2017). Simultaneously, the northern Atlantic Ocean SST anoma- lies affect the latitudinal position and strength of the ITCZ, modulating monsoonal flow and rainfall distribution, a rela- tionship supported by Crespo et al. (2019) and Kucharski et al. (2016). During the JJA season, the influence of the northern equatorial Atlantic diminishes, while SST anomalies in the western equatorial Indian Ocean and the southern Madagas- car region become more pronounced. A dipole SST pattern: positive anomalies in the western Indian Ocean coupled with negative anomalies south of Madagascar, emerges as a key driver of rainfall variability, aligning with the known impacts of the Indian Ocean Dipole (IOD) on East African climate (Ummenhofer et al. 2009). Furthermore, Pacific Ocean conditions, particularly La Niña events, contrib- ute to modulating JJA rainfall. Though La Niña typically enhances rainfall in East Africa, its influence during JJA can vary, with weaker events producing more muted impacts, as highlighted by Jessica et al. (2019). Figure 8 extends this analysis to the September–Novem- ber (SON) and December–February (DJF) seasons, reveal- ing significant spatial and temporal rainfall variability. The first three REOF modes explain 92.4% and 96% of the total variance during SON and DJF, respectively. South Omo remains a dominant center of variability in both sea- sons, with the Wolayita-Sidama border and Gofa areas also notably higher during the MAM season, a crucial period for maize growth, which could intensify heat stress and evapo- transpiration, thereby threatening crop yields in an already vulnerable agroecosystem. These temperature trends align with broader regional observations of increased frequency and intensity of hot spells during cooler months (Perkins-Kirkpatrick et al. 2012), exacerbating risks to water availability and crop resilience. The shift toward warmer nights during SON may increase crop respiration rates, reduce photosynthetic effi- ciency and potentially shorten maize growing cycles (Chan- dra et al. 2023; Dong et al. 2021; Lizaso et al. 2018; Rezaei et al. 2023). Collectively, these findings highlight a significant departure from historical climatic conditions with serious implications for maize productivity, water management, and ecosystem health in Southern Ethiopia. They reinforce the urgency for climate adaptation strategies, including devel- opment of heat-tolerant crop varieties, improved irrigation management, and climate-smart agricultural practices, to safeguard food security in the face of accelerating regional warming (Alemayehu et al. 2024b; Agnolucci et al. 2020). 3.2.2  Climate Spatiotemporal Variabilities and Potential Causes Understanding the spatiotemporal variability of climate, particularly rainfall and temperature, is crucial for manag- ing the agricultural and water resources of southern Ethio- pia. Seasonal fluctuations in precipitation and temperature profoundly influence crop growth cycles, water availability, and ecosystem health. This subsection examines the domi- nant patterns of seasonal climate variability, identifying key spatial hotspots and temporal trends. By linking these patterns with large-scale ocean-atmosphere drivers: such as sea surface temperature anomalies in the Indian and Atlantic Oceans with Congo basin and climate phenomena like ENSO and the Indian Ocean Dipole, we explore the underlying mechanisms shaping regional climate variabil- ity. These insights provide a vital foundation for improving seasonal forecasts, developing climate adaptation strategies, and enhancing resilience in climate-sensitive sectors. Figure 6 provides a detailed examination of the dominant spatial and temporal patterns of rainfall variability over the study region during the two main rainy seasons: March– May (MAM) and June–August (JJA). Employing Rotated Empirical Orthogonal Function (REOF) decomposition, the analysis identifies the leading modes of rainfall variability that collectively explain approximately 89.1% of the total seasonal variance for MAM and 89.2% for JJA (Table 1). The spatial patterns revealed by these modes are region- ally distinct. During MAM, the first mode is predominantly associated with South Omo, the second mode centers on the 1 3 T. T. Zeleke et al. adaptation measures, such as adjusted planting dates, irriga- tion scheduling, and crop variety selection. The oceanic connections identified through SST-rainfall correlations provide further insight into the mechanisms driving these seasonal rainfall fluctuations. During SON, positive correlations with equatorial Indian Ocean SSTs suggest that warmer ocean conditions enhance rainfall by increasing moisture transport and convective activity, con- sistent with earlier studies (Saji et al. 1999; Williams et al. 2012). Conversely, negative SST anomalies in the south- central Atlantic Ocean appear to suppress rainfall, likely through atmospheric circulation shifts that reduce moisture inflow. This interplay between positive and negative SST showing significant but seasonally shifting rainfall patterns. These findings illustrate the dynamic nature of rainfall spa- tial heterogeneity throughout the year and underscore the importance of seasonal-specific analyses for effective agri- cultural and water resource management. The coefficient variation (CV) analyses for rainfall and temperature complement these spatial-temporal findings, highlighting that variability is particularly pronounced during SON and DJF. Such variability poses substantial challenges for agricultural production, especially during the secondary cropping season, as erratic rainfall can lead to moisture stress and yield fluctuations. Understanding these spatially distinct patterns enables better targeting of Fig. 6  The dominant spatial and temporal patterns of rainfall variability across the study area during the two principal rainy seasons: MAM and JJA 1 3 Advancing Spatiotemporal Analysis of Climate Variability: Current Trends, Future Projections, Causes, and… smallholder farmers reliant on rainfed agriculture (Funk et al. 2008, 2018). To mitigate these risks, integrated adapta- tion strategies are essential. These include the development of localized early warning systems informed by oceanic and atmospheric monitoring, the promotion of drought- and heat-tolerant crop varieties, and the adoption of efficient water management practices such as rainwater harvesting and drip irrigation (Islam et al. 2025). Building community capacity to interpret climate information and adjust agri- cultural practices accordingly is equally vital for enhancing resilience. The spatiotemporal characterization of rainfall variability and its oceanic drivers provided here offers a robust foundation for tailoring climate-smart interventions, ensuring sustainable agricultural productivity and food security in southern Ethiopia amid a changing climate. 3.2.3  Impact on Maize Production Maize is a staple crop and a critical source of food security and livelihoods in southern Ethiopia. However, maize pro- duction in this region is increasingly challenged by climate variability, particularly fluctuations in rainfall and tempera- ture during key growth stages (Table 2). Understanding how these climatic factors interact with maize yield, production volume, and cultivated area is essential for assessing agri- cultural vulnerability and resilience. This section examines the spatiotemporal relationships between seasonal rainfall anomalies and maize production metrics across the key agricultural zones of Sidama, South Omo, and Wolayita from 2007 to 2023 (Table 2, Fig. 9). Evidence from Table 2 underscores the significant influence of rainfall onset and cessation timing on maize productivity, highlighting the sensitivity of the crop to shifts in seasonal rainfall distribu- tion. Complementarily, Fig. 9 illustrates a strong coherence between wet-season rainfall anomalies and positive maize production outcomes, emphasizing the direct responsive- ness of maize yields to favorable climatic conditions. By integrating climate records with crop production data, this analysis demonstrates how variability in rainfall and tem- perature shapes cultivation dynamics and production perfor- mance. The findings provide critical insights for designing targeted adaptation measures aimed at strengthening resil- ience, reducing production risks, and safeguarding food security in the face of evolving climate pressures. Figure 9 illustrates that maize production in Sidama, South Omo, and Wolayita has followed a generally upward trajectory over the 17-year period examined. On average, annual production increased by approximately 3,230 tonnes in Wolayita, 7,691 tonnes in South Omo, and 4,910 tonnes in Sidama. These gains can be attributed to several inter- related factors, including the expansion of cultivated land driven by rapid population growth (Food and Agricultural anomalies points to complex ocean-atmosphere dynamics that influence drought and flood risk in the region. During the DJF season, SST patterns are more spatially constrained but remain influential. Positive SST anomalies in the equatorial Indian and eastern Pacific Oceans contrast with cooler SSTs in the southeastern Pacific, resulting in a complex set of interactions that can drive erratic rainfall patterns. These conditions heighten the unpredictability of rainfall during the dry season, potentially jeopardizing off- season agricultural activities and water availability. The observed linkages between oceanic SST anomalies and rainfall variability underscore the vulnerability of south- ern Ethiopia’s climate system to global oceanic changes (see Table 1). With ongoing climate change, projections indicate that warming SSTs; particularly in the Indian Ocean will intensify rainfall variability, exacerbating challenges for Table 1  Summarizes the variance explained and temporal behavior of the leading REOF modes with its SST linkage Season Mode Variance Explained (%) Temporal Behaviour (RPC) Remarks/Link to Drivers MAM RPC1 35.9 Long-term trend & Interannual fluctuations Strong positive linkage with West-Pacific, north equato- rial Atlantic, West IOD RPC2 28.2 Interannual fluctuations RPC3 25 Long-term trend & Interannual fluctuations JJAS RPC1 35.7 Long-term trend & Interannual fluctuations Strong positive linkage with equatorial Indian ocean. RPC2 28.6 Interannual fluctuations RPC3 24.9 Long-term trend & Interannual fluctuations SON RPC1 49.5 Long-term trend & Interannual fluctuations Strong positive linkage with East equatorial Pacific, north equatorial Atlantic, West IOD, with high coefficient of determination than other seasons. RPC2 30.4 RPC3 12.5 DJF RPC1 55.6 Interannual fluctuations Medium posi- tively linked with the north equatorial sea surface temper- ature (SST) RPC2 38.7 RPC3 1.7 1 3 T. T. Zeleke et al. grain-filling periods. These stages are highly sensitive to rainfall distribution and temperature fluctuations, which directly determine germination success, vegetative growth, and grain development (Fig. 9b; Table 2). The strong yield– climate link identified here is consistent with evidence from broader regional studies showing that even moderate devia- tions in rainfall onset, intensity, or duration can substantially reduce productivity in rain-fed farming systems (Ademe et al. 2020; Stuch et al. 2020). Such sensitivity is not unique to southern Ethiopia but reflects wider patterns across Sub- Saharan Africa, where climate variability has been identified as one of the foremost drivers of yield instability in staple crops (Schlenker & Lobell 2010; Thornton et al. 2014; Lip- per et al. 2014). For instance, delayed onset or early cessa- tion of rainfall shortens the growing season and constrains biomass accumulation, while heat stress during grain fill- ing accelerates senescence and diminishes kernel weight (Lobell et al. 2011; Cairns et al. 2013). Consequently, the interplay between rainfall anomalies and temperature extremes remains a decisive factor shaping agricultural out- comes in the region. Particularly, in Sidama, a declining trend in harvested maize area is observed, which may indicate shifts in Organization of the United Nations 2017), the adoption of improved agricultural inputs such as high-yielding seed varieties and fertilizers (Vanlauwe et al. 2011; Spielman et al. 2012; Abate et al. 2015), as well as government-led programs promoting soil and water conservation, irriga- tion development, and smallholder intensification (Dercon & Christiaensen 2011; Berhe et al. 2022). Nevertheless, the upward trend in production has not been linear. It is fre- quently disrupted by pronounced interannual variability, largely shaped by climatic fluctuations, particularly rainfall anomalies during sensitive growth stages (Table 2). Such variability underscores the vulnerability of rain-fed maize systems to climate shocks in Ethiopia and across Sub-Saha- ran Africa more broadly (Thornton et al. 2014; Lipper et al. 2014; Kassie et al. 2015; J Ramirez-Villegas & Thornton 2015). These findings highlight the dual reality of steady long-term growth supported by agronomic and policy inter- ventions; alongside persistent risks associated with climate variability that continue to constrain production stability and food security outcomes in the region. Like production, significant correlations were observed between maize yield and key climatic variables during critical phenological stages, particularly the planting and Fig. 7  The statistically significant Pearson correlation patterns (p-value < 0.05) between the dominant seasonal rainfall principal components (RPCs) and global Sea Surface Temperatures (SSTs) over period from 1981 to 2024 1 3 Advancing Spatiotemporal Analysis of Climate Variability: Current Trends, Future Projections, Causes, and… Table 2  Distance correlation of maize production with monthly and seasonally rainfall (2007-2023) Area January February March April May June July August September October Sidama 0.314 0.486 0.403 0.416 0.383 0.414 0.379 0.472 0.419 0.404 South Omo 0.271 0.431 0.592 0.433 0.518 0.502 0.347 0.358 0.407 0.524 Wolayita 0.292 0.352 0.324 0.499 0.483 0.328 0.357 0.455 0.333 0.442 November December MAM JJA SON DJF FMAM JJAS ONDJ MAMJJAS Sidama 0.496 0.301 0.543 0.431 0.427 0.443 0.555 0.366 0.503 0.431 South Omo 0.449 0.325 0.688 0.233 0.479 0.454 0.681 0.341 0.526 0.509 Wolayita 0.393 0.246 0.358 0.423 0.442 0.352 0.366 0.362 0.542 0.317 Fig. 8  The dominant spatial and temporal modes of rainfall variability during the SON and DJF seasons across the study area, derived using Rotated Empirical Orthogonal Function (REOF) analysis 1 3 T. T. Zeleke et al. limiting productivity in many parts of East Africa, under- scoring the role of soil health in modulating climate impacts on agriculture (Amare and Melkamu 2020). The analysis also highlights a strong negative relationship between maize yield and declining rainfall trends, particularly pronounced in Wolayita and South Omo. Reduced seasonal precipitation leads to substantial yield declines, reflecting maize’s high sensitivity to water availability. These findings corroborate research indicating that fluctuations in rainfall timing and intensity critically affect crop success in rain-dependent sys- tems (DeLonge et al. 2016; Gupta et al. 2025). Maize yield variability is closely tied to several rainfall characteristics, including seasonal distribution, total amount, intensity, and the timing of onset and cessation. These factors are pivotal during sensitive growth stages such as flowering and grain filling, where water stress can severely reduce yields. Opti- mal rainfall during these phases is associated with higher productivity, whereas drought or erratic precipitation leads to sharp yield reductions, consistent with prior findings cropping patterns, possibly reflecting farmer adaptation to economic pressures or changing climatic conditions. Such land use transitions are consistent with observations elsewhere in sub-Saharan Africa, where farmers respond to multifaceted challenges by altering crop choices or expanding cultivation into alternative areas (DeLonge et al. 2016; Mir et al. 2025). In the region the weaker correlation between rainfall variability and yield (Table 2) may suggest that non-climatic factors such as soil fertility, irrigation, and improved agronomic practices may help buffer maize pro- duction against adverse climate effects (Duffy and Masere 2015). This underscores the importance of integrated man- agement approaches combining climate adaptation with soil and water conservation to stabilize crop yields under vari- able conditions. Whereas in Wolayita a marked decrease in maize yield (Fig. 9b) obtained, which may be linked to soil degrada- tion and declining fertility. Soil erosion, nutrient depletion, and unsustainable land management are recognized factors Fig. 9  The maize production (in tonnes), yield, and harvested area (in hectares) for the regions of Sidama, South Omo, and Wolayita, selected due to data availability, alongside growing season rainfall anomalies (March to September) from 2007 to 2023 1 3 Advancing Spatiotemporal Analysis of Climate Variability: Current Trends, Future Projections, Causes, and… policy decisions that aim to sustain livelihoods under evolv- ing environmental conditions. Figure 10 illustrates the bias patterns in projected rainfall relative to historical observations across Southern Ethio- pia, focusing on the SSP1 low-emission scenario. Monthly rainfall bias maps reveal where climate models tend to sys- tematically overestimate or underestimate precipitation, providing crucial context for evaluating the reliability of projections. Seasonal analyses using both global clima- tological and Ethiopian agricultural calendars emphasize periods critical for crop growth, such as the March–May (MAM) and September–November (SON) seasons. The results indicate a pronounced decrease in rainfall during the MAM season, especially over the South Omo zone, a region already sensitive to rainfall variability due to ocean-atmosphere interactions in the equatorial Indian Ocean and north/south near equator Atlantic Ocean. This projected reduction aligns with historical variability pat- terns, suggesting potential exacerbation of water stress during maize’s flowering and grain-filling stages, with sig- nificant implications for yields. Conversely, rainfall dur- ing SON is projected to increase, which could enhance the viability of a second cropping season but may also introduce risks of waterlogging and pest outbreaks if not managed carefully. Figure 11 details monthly and seasonal bias patterns for maximum temperatures, comparing projections under both SSP1 and SSP5 scenarios with historical data. A clear warming trend is evident across all seasons, with the MAM season experiencing slightly higher increases in daytime temperatures. Rising temperatures during this critical grow- ing season can intensify evapotranspiration, exacerbate soil moisture deficits, and increase heat stress on maize crops, potentially compounding the adverse effects of reduced rainfall. The contrast between SSP1 and SSP5 scenarios among different model outputs highlights the magnitude of future warming, reinforcing the urgent need for mitigation and adaptation efforts. Regions like South Omo, which already show vulnerability to climatic fluctuations, may face height- ened risks of drought and heat-related crop failures. Adapta- tion strategies: including drought-tolerant maize varieties, adjusted planting calendars, and improved water manage- ment will be essential to buffer these impacts. Together, the projected changes in rainfall and tem- perature underscore a complex future climate landscape in Southern Ethiopia, characterized by reduced and more variable rainfall during critical growing periods, coupled with rising heat stress. While increased rainfall during SON offers potential benefits for extended cropping opportuni- ties, the challenges posed by altered rainfall distribution and warming temperatures demand integrated, flexible in East Africa (Zeleke et al. 2023; Camberlin et al. 2009; Omay et al. 2023; Wakjira et al. 2021). Overall, the findings reveal that maize production in southern Ethiopia is highly vulnerable to spatiotemporal climate variability, particularly fluctuations in rainfall dur- ing critical growth stages. The interplay between climatic stressors and soil conditions further modulates yield out- comes, emphasizing the need for multi-faceted adaptation strategies. These may include drought and heat resilient maize varieties, improved soil fertility management, cli- mate-informed planting calendars, and enhanced water conservation to sustain maize productivity and support food security in the face of climate change (Shiferaw et al. 2014; Food and Agricultural Organization of the United Nations 2018). 3.2.4  Future Climate Characteristics As the global climate continues to change, it is imperative to understand how these shifts will manifest regionally, par- ticularly in climate-sensitive areas like Southern Ethiopia. This section explores projected future climate character- istics based on downscaled outputs from multiple climate models under two distinct Shared Socioeconomic Pathways (SSPs): SSP1, representing a low-emissions, sustainabil- ity-focused future, and SSP5, depicting a high-emissions, fossil-fueled development trajectory. Comparing projected climate conditions for 2020–2050 against historical base- lines (1990–2020), we identify critical trends and biases in rainfall and temperature that have direct implications for agriculture, water resources, and broader ecosystem ser- vices for long term plan (Marshall et al. 2025). The analysis provides a detailed examination of seasonal and monthly variations in rainfall and temperature, reveal- ing where and when significant changes are likely to occur. Understanding these shifts is essential for anticipating their impact on maize production, a staple crop vital for regional food security and enable for prior preparation. However, short-term year/season ahead prediction using those driv- ers described in the previous section increases the certainty of the result. Changes in precipitation patterns, such as decreases in critical growing-season rainfall or increases in rainfall variability combined with rising temperatures, pose complex challenges to crop growth, soil moisture availabil- ity, and pest dynamics. Conversely, projected increases in rainfall during other seasons may present opportunities if effectively harnessed through adaptive management. Inte- grating climate model projections with agronomic under- standing, this section offers a comprehensive perspective on future climate risks and opportunities. These insights are foundational for guiding climate-smart agricultural strate- gies, developing resilient cropping systems, and informing 1 3 T. T. Zeleke et al. Fig. 10  The bias patterns of projected (2020-2050) versus historical (1990-2020) rainfall across South Ethiopia, derived from a multi- model ensemble mean under the Shared Socioeconomic Pathway 1 (SSP1) scenario, often referred to as a sustainability-focused, low- emissions pathway scenario 1 3 Advancing Spatiotemporal Analysis of Climate Variability: Current Trends, Future Projections, Causes, and… 4  Summary and Conclusion To contextualize the study area’s climate, we analyzed long‐term (1981–2024) rainfall, minimum temperature, and maximum temperature patterns across Gofa, Wolay- ita, South Omo, and Sidama. These variables were chosen for their direct relevance to crop growth, water availability, and agricultural decision‐making. Seasonal and spatial cli- matologies were assessed to establish baseline conditions, management approaches. Building resilience will require combining climate-smart agricultural practices, enhanced soil and water conservation, and strengthened early warning systems. Understanding and anticipating these future cli- mate characteristics enables policymakers, researchers, and farmers to collaboratively develop proactive strategies that safeguard maize production and support sustainable liveli- hoods in the face of climate change. Fig. 11  The monthly and seasonal bias patterns of maximum temperature projections from ensemble climate models, comparing 2020–2050 (pro- jection) with 1990–2020 (historical) data, under SSP1 (sustainable pathway) scenario 1 3 T. T. Zeleke et al. making early warning systems vital for building resilience amid escalating climate uncertainty. Maize yields in Wolayita, South Omo, and Sidama exhibit strong sensitivity to seasonal rainfall variability and tem- perature extremes during critical growth periods. Although overall production trends show an increase, year-to-year climate fluctuations contribute to notable yield instability. Reductions in harvested areas in certain regions indicate adaptive or economic responses to both climatic and socio- economic pressures, while soil degradation, especially in Wolayita, further challenges productivity. Integrating maize production data (from 2007–2023) with seasonal rainfall anomalies, this analysis uncovers complex interactions between climate variability and farming practices, includ- ing farmers’ dynamic land-use adjustments to changing moisture conditions. This integrated approach is essential for assessing the vulnerability and resilience of maize sys- tems, informing targeted strategies such as drought-tolerant crop adoption, revised planting schedules, and enhanced water management. Ultimately, strengthening the link- age between climate monitoring and agricultural planning, through improved forecasting, water storage infrastructure, and early warning systems, is critical to safeguarding food security and building climate resilience across southern Ethiopia amid increasing climatic uncertainty. Understanding future climate trajectories is vital for anticipating agricultural and ecological challenges in South- ern Ethiopia. Using multi-model ensembles under contrast- ing emission scenarios (SSP1 and SSP5), this analysis highlights key mid-century shifts in rainfall and temperature patterns affecting maize production and ecosystem health. Notably, projections indicate reduced rainfall during the critical MAM season, especially in South Omo, and ris- ing temperatures across all seasons, with more pronounced warming under SSP5. Increased SON rainfall may offer cropping opportunities but also raises risks of waterlog- ging and pest outbreaks. The combination of diminished growing-season precipitation and heightened heat stress underscores the urgent need for adaptive strategies, includ- ing drought-tolerant crops, adjusted planting schedules, and enhanced water management. These insights provide a foundation for proactive, climate-resilient planning to safe- guard food security and sustain livelihoods amid evolving climatic pressures. This study highlights the complexity and urgency of addressing climate change impacts on agriculture in South- ern Ethiopia, where significant spatial and temporal climate variability, driven by local and global factors threatens maize productivity, a cornerstone of food security and live- lihoods. Observed and projected warming trends coupled with shifting rainfall patterns demand integrated adaptation distinguish distinct climatic regimes, and quantify the mag- nitude and timing of critical phases. Zonal annual cycles complemented the spatial analyses by revealing intra‐ annual variability and identifying periods of climatic stress or opportunity for agriculture. Such baselines are essential for detecting anomalies, evaluating interannual variability, and interpreting the potential impacts of climate change on maize‐based farming systems in Southern Ethiopia. The analysis revealed pronounced spatial heteroge- neity in rainfall and temperature regimes, with clearly defined seasonal patterns across key agricultural zones. The region exhibits a bimodal rainfall distribution, vital for rainfed maize cultivation, characterized by variability in onset, intensity, and duration. Temperature patterns fol- lowed a well-defined annual cycle, with distinct warming and cooling phases evident in both daytime and nighttime temperatures. Long‐term analysis reveals statistically significant warm- ing across all seasons in Southern Ethiopia, with temperature increases from moderate to high, even during traditionally cooler months, posing emerging risks to agricultural pro- ductivity and ecosystem stability. Rainfall trends are spa- tially and seasonally variable, with notable declines in parts of South Omo during MAM and a significant SON increase across much of the region, potentially linked to shifting ocean–atmosphere dynamics. While enhanced SON rainfall may boost water availability and support maize during the secondary season, rising temperatures heighten heat stress, accelerate crop development, and intensify water demand. These findings highlight the urgent need for localized cli- mate assessments, adaptive agricultural calendars, heat‐ tolerant crop varieties, and improved water management to sustain yields and rural livelihoods. Integrating climate forecasts into farm planning will be essential for building resilience in the face of rapid and complex climate change. Rotated Empirical Orthogonal Function (REOF) analy- sis revealed that dominant modes of rainfall variability in southern Ethiopia are strongly influenced by global oceanic drivers, including ENSO, the Indian Ocean Dipole, and Atlantic SST anomalies near West Africa. These coupled ocean–atmosphere dynamics explain much of the intra‐ and interannual rainfall fluctuation, with pronounced impacts on agricultural productivity. Seasonal analysis identified South Omo, Wolayita–Sidama, and Gofa as hotspots of variability during both main rainy seasons (MAM, JJA) and the shorter SON and DJF periods. Ocean temperature anomalies in the Indian and Atlantic Oceans modulate rainfall timing and intensity, altering planting windows and water availability. Such variability increases the risk of both drought and flood- ing, threatening maize yields and food security. Enhancing the integration between local climate patterns and global/ regional oscillations will improve forecast reliability, 1 3 Advancing Spatiotemporal Analysis of Climate Variability: Current Trends, Future Projections, Causes, and… source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit ​h​t​t​p​​:​/​/​​c​r​e​a​​t​i​​v​e​c​​o​m​m​o​​n​s​.​​o​ r​g​​/​l​i​c​e​n​s​e​s​/​b​y​/​4​.​0​/. 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Collaboration among policymakers, extension services, and farming communities is essential to translate climate insights into actionable strategies that sustain production and bolster resilience. The findings underscore the critical need for adopting drought-tolerant crops, upgrading irriga- tion infrastructure, and promoting community-based water management. Early warning systems and climate-smart agricultural practices must be expanded to enable farmers to anticipate and respond effectively to climate risks. Coor- dinated efforts grounded in localized climate data will drive targeted interventions to strengthen agricultural resilience and safeguard food security. Ultimately, this study advances understanding of the complex climate-agriculture nexus in Southern Ethiopia and provides a foundation for adaptive strategies that ensure sustainable maize production and community resilience. Ongoing research in climate modeling, crop simulation, and innovative agricultural practices will be vital to navigating future uncertainties and securing livelihoods for smallholder farmers across the region and beyond. Supplementary Information  The online version contains supplementary material available at ​h​t​t​p​​s​:​/​​/​d​o​i​​.​o​​r​g​/​​1​0​.​1​​0​0​7​​/​s​4​​1​7​4​8​-​0​ 2​5​-​0​0​9​2​1​-​7. Acknowledgements  The first author would like to acknowledge the support of the Alliance of Bioversity International and the Interna- tional Center for Tropical Agriculture (CIAT). Funding  Project CGIAR Trust fund; through the Regional Integrated Initiative for Diversification in East and Southern Africa and the Scal- ing for Impact Science Program Data availability  The data supporting the findings of this study are available from the corresponding author upon reasonable request. Some data may be subject to third-party restrictions. Declarations Competing Interests  The authors declare that they have no competing interests. There are no financial or personal relationships that could influence the results or interpretation of this study. Consent for Publication  The authors confirm that all individuals in- volved in this study have provided their consent for the publication of the results. No personally identifiable information is included in this manuscript, ensuring the privacy and confidentiality of all participants. Open Access  This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the 1 3 http://creativecommons.org/licenses/by/4.0/ http://creativecommons.org/licenses/by/4.0/ https://doi.org/10.1007/s12571-015-0488-z https://doi.org/10.1007/s12571-015-0488-z https://doi.org/10.1016/j.wace.2020.100263 https://doi.org/10.1016/j.wace.2020.100263 https://doi.org/10.1038/s43016-020-00148-x https://doi.org/10.1038/s43016-020-00148-x https://doi.org/10.3390/land13111915 https://doi.org/10.3390/land13111915 https://doi.org/10.1108/IJCCSM-08-2024-0133 https://doi.org/10.1108/IJCCSM-08-2024-0133 https://doi.org/10.18805/ag.R-136 https://doi.org/10.18805/ag.R-136 https://doi.org/10.1016/j.agwat.2022.107959 https://doi.org/10.1016/j.agwat.2022.107959 https://doi.org/10.1007/s12571-013-0256-x https://doi.org/10.1007/s12571-013-0256-x https://doi.org/10.1007/s00704-009-0113-1 https://doi.org/10.1007/s41748-025-00921-7 https://doi.org/10.1007/s41748-025-00921-7 T. 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T. Zeleke et al. Publisher's Note  Springer Nature remains neutral with regard to juris- dictional claims in published maps and institutional affiliations. Zeleke TT, Lukwasa AZW, Ture KB, Ayal DY (2024) Analysis of spatio-temporal precipitation and temperature variability and trend over Sudd-Wetland Republic of South Sudan. Climate Serv 34:100451. ​h​t​t​p​s​:​​​/​​/​d​o​​i​.​o​​r​​g​​/​​1​0​​.​1​0​​​1​​6​​/​j​.​c​l​i​​s​e​r​.​​2​0​2​4​.​1​0​0​4​5​1 1 3 https://doi.org/10.1016/j.cliser.2024.100451 Advancing Spatiotemporal Analysis of Climate Variability: Current Trends, Future Projections, Causes, and Impacts on Maize Production in Southern Ethiopia Abstract Highlights 1 Introduction 2 Methodology 2.1 Study Area 2.2 Data Source 2.3 Statistical Methods 3 Results and Discussion 3.1 Spatiotemporal Analysis of Climate Variables 3.2 Climate Change and Variability 3.2.1 Climate Trends 3.2.2 Climate Spatiotemporal Variabilities and Potential Causes 3.2.3 Impact on Maize Production 3.2.4 Future Climate Characteristics 4 Summary and Conclusion References