https://orcid.org/0000-0003-2325-4284
Figures
Abstract
Rainfall variability and change are critical drivers of food production in tropical regions. This study analyzed rainfall trends and variability in the drylands of Wolaita Zone (DLWZ), southern Ethiopia. It used observed rainfall data (1990–2022) and projections for 2026–2075 under two shared socioeconomic pathways (SSP2-4.5 and SSP5-8.5). Rainfall variability was assessed using the coefficient of variation, while trends were examined with the modified Mann–Kendall test and Sen’s slope. Results show extremely high (>30%) to high (20–30%) seasonal and inter-annual variability during 1990–2022, with decreasing rainfall trends except for winter, which showed a non-significant increase. Projections indicate medium (10–20%) to high variability in annual rainfall and high to extremely high variability in seasonal rainfall. Significant decreases are expected in spring rainfall, while summer and winter rainfall are projected to increase, particularly in 2051–2075. These findings suggest that persistent variability and declining rainfall during critical seasons may intensify risks to agricultural production in southern Ethiopia. The dataset provides a valuable basis for future studies on the impacts of rainfall change on crop and livestock systems.
Citation: Chinasho A, Tesfaye H, Yirgu Z, Shanka D (2026) Observed and CMIP6 projected rainfall variability and change in drylands of southern Ethiopia. PLOS Clim 5(1): e0000800. https://doi.org/10.1371/journal.pclm.0000800
Editor: Ahmed Kenawy, Mansoura University, EGYPT
Received: June 16, 2025; Accepted: December 12, 2025; Published: January 5, 2026
Copyright: © 2026 Chinasho et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The datasets used and/or analyzed during the current study are submitted as supplementary materials.
Funding: This research was fully funded by Wolaita Sodo University. A total of 150,000 Ethiopian Birr was provided to the four authors—AC, HT, ZY, and DS—to support the implementation of the study. The funding institution had no involvement in the design of the study; the collection, management, analysis, or interpretation of the data; the preparation, review, or approval of the manuscript; or the decision to submit the article for publication. All aspects of the study were conducted independently by the authors.
Competing interests: The authors have declared that no competing interests exist.
1. Introduction
Agricultural production systems in developing countries, including Ethiopia, are highly sensitive to climate change and variability because of their strong dependence on rain-fed farming [1,2]. Rainfall is naturally intermittent, showing marked spatio-temporal variation and irregular trends across regions and timescales. According to the EPA [3], global annual precipitation increased non-significantly by about 1.02 mm per decade during 1901–2021. However, regional patterns differ, with some areas experiencing intensification of rainfall and others severe declines, underscoring the need for localized studies. Timely and reliable information on rainfall change and variability is therefore crucial for guiding adaptation strategies in agriculture and enhancing food security.
Global Climate Models (GCMs) remain the primary tools for simulating precipitation under climate change, but their coarse spatial resolution and model biases limit their reliability at local scales [4–6]. Downscaling, whether statistical or dynamical, has thus become indispensable for generating usable rainfall projections. However, no single method performs equally well everywhere, and the choice depends on the research objectives, available data, and intended applications [7,8].
Rainfall plays a pivotal role in sustaining livelihoods in African drylands, where agriculture is predominantly rainfed [9]. Yet rainfall in these regions is highly variable on annual, seasonal, and spatial scales, resulting in recurrent droughts, floods, and reduced vegetation cover [10–12]. Recent studies confirm strong variability across the Horn of Africa, including Ethiopia, Kenya, and Somalia, particularly in the onset, cessation, and length of rainy seasons [13]. In Ethiopia, rainfall trends are spatially and temporally inconsistent: some studies report declining annual and seasonal rainfall in northern and central regions [14–16], while others note increases in parts of the Rift Valley and southern Ethiopia [17,18]. For example, rainfall has been shown to increase during summer but decline in spring and winter at stations such as Hosana and Bilate in southern Ethiopia [18].
Despite these insights, most previous studies have focused on historical rainfall patterns, often relying on older Representative Concentration Pathways (RCPs), which are being replaced by the more refined Shared Socioeconomic Pathways (SSPs). Moreover, limited empirical work has been carried out on the variability and future changes of rainfall in the drylands of southern Ethiopia, despite their high vulnerability to climate extremes and agricultural importance [19–21].
This study therefore investigates the long-term rainfall variability and changes in the drylands of Wolaita Zone (DLWZ), southern Ethiopia, using observed records (1990–2022) and future projections (2026–2075) under SSP2-4.5 and SSP5-8.5 scenarios. In this study, rainfall variability refers to the deviation from mean values, whereas change refers to percent deviation from the baseline and tend. By providing updated and locally relevant insights, the study aims to contribute to climate-resilient agricultural planning and to support progress toward Sustainable Development Goals (e.g., climate action and zero hunger).
2. Materials and methods
2.1. Description of the study area
2.1.1. Location.
Bele (Kindo Koysha), Bilate (Duguna Fango), Gessuba (Ofa), and Humbo (Humbo) meteorological stations are situated in the dryland landscapes of the Wolaita Zone (Fig 1). These stations capture climatic variations across distinct locations, making them valuable for assessing rainfall variability, land-use dynamics, and agricultural responses to climate stressors.
The map was prepared using ArcGIS software (version 10.7), with shapefiles obtained from the Ethiopia Mapping Agency [22].
Kindo Koysha district borders Dawro and Koysha districts and spans elevations around 1,500 m a.s.l. The district’s administrative seat, Bele (Bale Hawassa), also hosts the Bele meteorological station (6.918° N, 37.526° E; 1,240 m a.s.l.). The station is located roughly 266 km south–southwest of Addis Ababa and 27 km west–northwest of Wolaita Sodo. Duguna Fango district is positioned on the eastern side of Wolaita, facing the Bilate River and the Central Rift Valley, which strongly influences its hydro-climate and land-use dynamics. The district seat, Bitena, is about 42 km east of Wolaita Sodo and 300 km south of Addis Ababa by road. Within this district lies the Bilate meteorological station (6.822° N, 38.088° E; 1,361 m a.s.l.), located approximately 251 km south–southwest of Addis Ababa and 36 km east of Wolaita Sodo.
Ofa district, on the southwestern flank of Wolaita, borders Kindo Koysha and Kindo Didaye districts. Its administrative seat is Gessuba, where the Gessuba station is located. The Gessuba station (6.729° N, 37.563° E; 1,552 m a.s.l.) is situated 26 km west–southwest of Wolaita Sodo and 283 km south–southwest of Addis Ababa. Humbo district stretches across Rift Valley lowlands and mid-altitude uplands, it offers diverse ecological conditions. The district seat, Tebela, hosts the Humbo station (6.703° N, 37.766° E; 1,618 m a.s.l.). This station is roughly 276 km south–southwest of Addis Ababa and 17 km south of Wolaita Sodo.
2.1.2. Climate, geology, and soil.
The monthly average rainfall of the drylands of the Wolaita Zone (DLWZ) follows a bimodal pattern [23]. As shown in Fig 2, the mean monthly rainfall over the past 33 years (1990–2022) ranged from 24.2 mm to 140.6 mm. Corresponding average monthly temperatures ranged from 12.4–15.3 °C at night and 26.03–31.38 °C during the day. Rainfall distribution across seasons is a defining feature of the agro-climatic conditions of Ethiopia’s dryland regions. In the DLWZ, pronounced seasonal variability and uneven rainfall distribution critically shape agricultural practices and determine crop productivity. Long-term meteorological records confirm that rainfall is not evenly distributed throughout the year, with different seasons contributing disproportionately to the annual total. This seasonal imbalance has a direct impact on rain-fed agriculture, which remains the dominant farming system in the region. The geology of the DLWZ is primarily composed of volcanic rocks of igneous origin [24]. The prevailing soils are Nitisols, whose formation is strongly influenced by aluminum and/or iron chemistry [25]. Texturally, the soils are predominantly clay [26].
2.1.3. Agriculture and its vulnerability to climate variability impacts.
The drylands of the Wolaita Zone (DLWZ) encompass diverse land-use and land-cover types, including agricultural land, forestland, grazing land, shrublands, bushlands, and water bodies. An agropastoral system serves as the primary source of livelihood, but the area is dominated by fragmented and small landholdings. The DLWZ is among the most climate-vulnerable areas compared to the Dega and Woyna Dega agro-ecologies [27,28]. Its high vulnerability arises from greater exposure and sensitivity to climatic shocks, combined with a limited adaptive capacity [29,30]. Farmers rely more heavily on short-term and less efficient adaptation strategies (e.g., as destocking, shifting planting dates) rather than on effective options like irrigation [28,31]. This reliance increases the likelihood of worsening vulnerability in the future. Adoption of sustainable practices remains low, with limited uptake of integrated soil fertility management, agroforestry, and soil and water conservation. Barriers such as resource constraints and institutional limitations hinder wider adoption. Furthermore, the DLWZ lacks adequate climate information services, unlike in developed regions, leaving farmers unprepared and contributing to recurrent losses in crop and livestock production [32]. To address these challenges, integrating historical climate data with future projections is essential. Such an approach would enable policymakers to design anticipatory and proactive adaptation strategies tailored to the region’s needs.
2.2. Acquisition of past rainfall data and quality control
Historical daily rainfall records for the Wolaita Zone drylands were obtained from the National Meteorological Institute of Ethiopia for the period 1990–2022. This study considered rainfall observation datasets from the Bele, Bilate, Gessuba, and Humbo stations, which were selected because they are located in climate change–impacted drylands and represent some of the most vulnerable areas. The datasets were first checked for quality, including assessments of missing values, outliers, and homogeneity. Addressing these issues is essential when analyzing climate variability and trends [33]. Missing values were filled using the simple arithmetic mean for temperature and multiple linear regression for rainfall. These gap filling methods are claimed by many previous studies including [34,35], for balancing simplicity, statistical appropriateness, and preservation of critical climatic characteristics. Outliers were removed using the ± 1.5 × interquartile range criterion. Homogeneity was assessed using the Standard Normal Homogeneity Test (SNHT), Pettitt, and Buishand tests. All preprocessing and quality control procedures were conducted using R-Instat software (version 0.7.5).
2.3. Mapping spatial distribution of rainfall
The station rainfall datasets for other locations of Wolaita Zone were interpolated using the ordinary kriging analysis tool in ArcGIS 10.7. The ordinary kriging was used because it models spatial autocorrelation via an empirical semivariogram to yield best linear unbiased estimates in areas where elevation covariate is available [36,37].
2.4. Acquisition of future rainfall data
The future-projected (2026–2099) daily rainfall (precipitation) datasets of six Coupled Model Intercomparison Project Phase 6 (CMIP6) were used in this study. The CMIP6 simulations were accessed from the World Climate Research Program (WCRP) official website. The future rainfall data under two commonly used Shared Socioeconomic Pathways (SSP2-4.5 and SSP5-8.5) were considered. These scenarios represent moderate and high greenhouse gas concentration trajectories, respectively, and are used to model potential climate futures based on varying levels of emissions and socio-economic developments. The six CMIP6 models considered include ACCESS-CM2, ACCESS-ESM1–5, EC-Earth3-Veg, HadGEM3-GC31-LL, MIROC-ES2L, and MPI-ESM1–2-HR. The selection of these CMIP6 models was guided by their demonstrated performance and widespread application in simulating the key climate processes over Eastern Africa [38,39]. These models are designed to capture large-scale atmospheric circulation, monsoon dynamics, and teleconnections that strongly influence rainfall variability in Ethiopia [40,41]. Previous evaluations have shown that these models reliably reproduce historical seasonal rainfall patterns and interannual variability over the Horn of Africa [38,42,43]. Moreover, the use of multi-model ensembles reduces structural uncertainty and improves the robustness of regional climate projections [40,41]. Thus, the chosen models provide credible first-order projections for assessing climate change impacts in the drylands of the Wolaita Zone, particularly when complemented with bias correction techniques [42,43].
2.5. Bias correction of CMIP6 outputs
The precipitation datasets were bias-corrected using power transformation (PT), multiplicative linear scaling (MLS), distribution mapping (DM), multiplicative delta change (MDC), and local intensity scaling (LIS) bias correction methods (BCMs). Bias correction was performed using the Climate Model Data for Hydrological Modeling (CMhyd) software. The performance of each BCMs was evaluated using the Nash–Sutcliffe efficiency (NSE), mean absolute error (MAE), and coefficient of determination (R2). NSE ranges from –∞ to 1, with higher values indicating better model performance. Smaller MAE values indicate improved accuracy, while R2 values of 1, between 0 and 1, and 0 represent perfect fit, partial fit, and no fit to observed data, respectively.
Among the BCMs, the MDC method consistently produced the highest NSE and R2 and the lowest MAE, demonstrating superior performance. The performance of other BCMs varied depending on the CMIP6 GCM and the performance metric, sometimes improving results, sometimes having no effect, or even increasing errors. MDC, in contrast, consistently improved all six CMIP6 GCMs across all three-performance metrics (Fig 3). For raw CMIP6 outputs, MAE, R2, and NSE ranged from 169.6–1321.7 mm, 0.005–0.17, and –72.88 to –0.626, respectively. After applying MDC, these values improved to 9.61–96.82 mm, 0.99, and 0.71–0.99, respectively, representing a minimum 96% reduction in error magnitude.
The improvement of rainfall datasets following bias correction aligns with previous studies [44–46]. Therefore, the application of the MDC method to precipitation records of the six CMIP6 GCMs over the drylands of Wolaita Zone is scientifically justified. Using the ensemble mean further reduced projection errors by 74.03%, 65.93%, 62.18%, and 79.73% compared to ACCESS-CM2, ACCESS-ESM1–5, EC-Earth3-Veg, HadGEM3-GC31-LL, and MIROC-ES2L, respectively. However, the ensemble mean increased errors by 6.38% compared to MPI-ESM1–2-HR. Overall, the ensemble mean proves effective in reducing errors and is recommended for this study area. The enhanced performance of the ensemble mean is consistent with observations in the Baro-Akobo basin of Ethiopia [44] and the Mékrou catchment of Benin [44,46].
2.6. Analysis of rainfall variability and change
The variability and change of rainfall were analyzed on both annual and seasonal scales. The analysis covered the recent past 33 years (1990–2022) and projected the future 50 years in two time slices: the 2040s (2026–2050) and the 2060s (2051–2075). Three Ethiopian seasons were considered in this study: winter (October–January, ONDJ), spring (February–May, FMAM), and summer (June–September, JJAS). Seasonal rainfall was calculated as the total rainfall of the respective months, while annual rainfall was obtained by summing all months of the year. Changes in rainfall relative to the baseline period (1990–2022) were expressed as percentages.
Rainfall variability was assessed using the coefficient of variation (%CV). Although %CV can become infinite or undefined when mean values approach zero, this is not a concern in the present study since the mean rainfall values are well above zero. Therefore, CV is suitable here and has also been applied in previous studies [47,48]. The 95% confidence interval (mean ±1.96*standard error) was used to clearly show the fluctuations between estimated values. According to Gomes [49], CV% was classified as follows: < 10% (low), 10–20% (medium), 20–30% (high), and >30% (very high).
Trends in rainfall were analyzed using the non-parametric modified Mann–Kendall trend test [50,51] and Sen’s slope estimator, implemented in R software version 4.0.5 [52]. The test results were interpreted such that negative values indicate a decreasing trend, zero indicates no monotonic trend, and positive values represent an increasing trend. Statistical significance was evaluated at the 95% confidence level.
3. Results and discussion
3.1. Seasonal contributions and distributions of rainfall in drylands of Wolaita Zone
Rainfall distribution in the Drylands of Wolaita Zone (DLWZ) shows pronounced seasonal variability consistent with a bimodal regime. During winter (October–January; Fig 4A), rainfall was minimal, with Bele station recording the highest total (205 mm). In spring (February–May; Fig 4B), Gessuba and its surroundings received the largest amount (>383 mm), while in summer (June–September; Fig 4C), nearly all stations experienced substantial rainfall above 450 mm, except Bilate, which remained comparatively drier. Seasonally, winter contributed the least (17.2%), whereas summer provided the maximum (up to 48.7%). Collectively, spring and summer contributed nearly 80.9% of the annual rainfall, confirming their importance in sustaining agricultural activities in the region.
This bimodal rainfall pattern is shaped by regional atmospheric circulation systems. During Belg (spring), the Somali Jet advects moisture from the western Indian Ocean toward southern Ethiopia [10,53], while the Turkana Jet provides additional inflow through the Turkana Channel, enhancing low-level convergence and convective activity [54,55]. In Kiremt (summer), the strengthening of the Tropical Easterly Jet supports strong upper-level divergence, facilitating deep convection and prolonged rainfall over the Ethiopian Highlands [56,57]. The combined effect of these circulation systems produces two distinct rainfall peaks each year, explaining the observed seasonal contrasts in DLWZ.
Annual rainfall in DLWZ ranged between 828 and 1127 mm, slightly higher than the regional mean (~1045 mm). This distribution aligns with findings from other parts of Ethiopia, where Belg and Kiremt dominate the rainfall regime. For example, spring and summer contributed more than 90% of annual totals in the Tana Basin [58], while similar dominance (≈89.8%) was reported for eastern and western Harerghe Zones [59]. Such consistency highlights the strong dependence of Ethiopian agriculture on these two rainy seasons.
Overall, the results emphasize that Belg and Kiremt rains are the primary rainfall periods governing soil preparation, planting, and crop growth in southern Ethiopia. However, their variability in onset, intensity, and cessation poses risks to farming systems, making agricultural activities highly vulnerable to rainfall fluctuations. These dynamics underscore the need for climate-smart strategies, including early warning systems, drought-tolerant crops, and adaptive planting calendars, to mitigate risks associated with seasonal variability.
3.2. Rainfall variability for the last 33 years in drylands of Wolaita Zone
Rainfall variability across the Drylands of Wolaita Zone (DLWZ) has been marked by pronounced seasonal fluctuations. Over the past three decades, spring at Gessuba station recorded the lowest coefficient of variation (CV) at 28.8%, while winter (Bega) at Humbo station registered the highest at 66.5% (Table 1). This highlights how critical seasons like spring, traditionally relied upon for crop planting, have become increasingly unreliable. This is characterized by erratic onsets, false starts, and intervening dry spells, all of which disrupt crop germination and land preparation. By contrast, the annual rainfall variability is relatively moderate, with CV values ranging from 17.3% to 30.1%. This suggests that while annual totals remain fairly consistent, the intra-annual distribution of rainfall is highly uneven. Even when cumulative yearly rainfall aligns with long-term averages, compressed or erratic seasonal distributions, or intense rainfall pulses, may trigger excessive runoff, erosion, and poor infiltration. Intra-annual extremes therefore pose more tangible threats to farming stability compared to overall interannual variability.
These patterns are not unique to DLWZ. Recent studies in Southwest Ethiopia show seasonal CVs spanning 21% to 122% [60], with spring and summer showing comparatively higher variation—highlighting systemic challenges in reliable rainfall patterns. In southern Ethiopia, a study revealed seasonal CVs of roughly 27% for spring and 24–53% across seasons, aligning closely with findings from DLWZ [61]. These parallel observations underscore that high seasonal variability is a widespread and persistent challenge across Ethiopia.
Spatial heterogeneity further complicates the situation: rainfall variability can shift drastically over short distances due to topography and localized weather dynamics [62]. Thus, smallholder farmers navigating agriculture in DLWZ face the compounding challenges of both temporal unpredictability and local spatial variability. Collectively, these findings emphasize that while annual rainfall remains fairly stable, the rising unpredictability of seasonal distribution undermines rain-fed agriculture. Adapting to this new reality demands climate-responsive strategies—namely, enhanced forecasting, water harvesting systems, flexible planting calendars, and resilient crop varieties—to reduce vulnerability to shifting rainfall patterns.
3.3. Rainfall trend in past 33 years in drylands of Wolaita Zone
Among all research sites, Bele station exhibited the most pronounced increase in both annual and seasonal rainfall—spring being the lone exception (Table 2). This upward trend was statistically significant (p < 0.05), distinguishing Bele from other sites in the DLWZ, where most showed non-significant or significantly declining rainfall patterns. Such spatial heterogeneity underscores the importance of site-specific climatic monitoring to capture localized climate dynamics.
Simultaneously, the non-significant increase in summer rainfall and corresponding non-significant decline in spring rainfall align with findings from the Awash River Basin, suggesting national-level climate trends with significant local variation [61]. Some of this variability may be driven by a weakening Walker Circulation, which enhances East African convective rainfall [63]. However, increased summer precipitation may not benefit agriculture uniformly. Excess rainfall can lead to waterlogging, reduced root oxygen, nutrient leaching, soil acidification, and elevated disease susceptibility. This is supported by observations in the Upper Blue Nile basin, where wetter conditions under future climate scenarios reduce wheat suitability due to moisture and temperature stress [64].
Moreover, heavy summer rains elevate risks of landslides and floods, hazards that threaten not just crops and livestock, but also communities and infrastructure. This scenario is especially concerning for hilly areas like Wolaita Zone, which are highly susceptible to slope failures under saturated conditions. At the same time, declining spring rainfall—poses direct threats to crop establishment and yields. These shifting seasonal patterns are detrimental to farmers’ scheduling and can contribute to food insecurity. The socio-economic implications are profound: Ethiopia’s economy is deeply dependent on agriculture, and climate variability already reduces economic output significantly. A paradigm shift toward climate-resilient strategies is imperative—especially adaptive approaches tailored to local climatic nuances.
3.4. Future rainfall variability projections in drylands of Wolaita Zone
Future projections highlight not only shifts in mean rainfall but, more critically, a substantial increase in variability across the drylands of Wolaita Zone (DLWZ) Fig 5). In the near-term (2026–2050), annual rainfall is expected to fluctuate between 940.8 mm and 1796.8 mm under SSP2-4.5 (95% CI: 1304.6 ± 84.4; Fig 5A). Under the SSP5-8.5 pathway, the range widens to 991.3–1898.2 mm (95% CI: 1380.1 ± 89.7) (S1 Data). By mid-century (2051–2075), annual rainfall is projected to span 854.0–2146.8 mm under SSP2-4.5 (95% CI: 1419.2 ± 112.5) and 1037.9–2620.7 mm under SSP5-8.5 (95% CI: 1745.9 ± 139.3; Fig 5B) (S2 Data). According to the variability classification of Gomes [49], this widespread places DLWZ rainfall firmly in the high variability category, underscoring the heightened risk of both extreme drought and flood years.
Seasonal variability is even more pronounced (Fig 5C–5H). During the 2026–2050 period, winter (historically the dry season) rainfall is projected at 104.1–658.1 mm (95% CI: 236.6 ± 46.6 under SSP2-4.5) and 114.8–707.1 mm (95% CI: 262.1 ± 50.2 under SSP5-8.5) (S1 Data). By 2051–2075, winter rainfall increases to 111.2–685.0 mm (95% CI: 260.6 ± 49.9 under SSP2-4.5) and 148.3–905.4 mm (95% CI: 353.4 ± 66.9 under SSP5-8.5) (S2 Data). With a coefficient of variation (CV) of 48–50%, the season shifts from being historically dry and relatively predictable to one of the most unstable. This volatility may bring either rare winter storms or prolonged dry spells, both of which would severely disrupt soil and water management.
Spring, the critical sowing season, also shows strong variability. Between 2026–2050, rainfall is projected at 431.2–1118.5 mm (95% CI: 643.3 ± 65.7 under SSP2-4.5) and 446.6–1165.6 mm (95% CI: 674.1 ± 69.0 under SSP5-8.5). By 2051–2075, the range expands to 260.0–1157.6 mm (95% CI: 691.5 ± 100.2 under SSP2-4.5) and 324.2–1390.9 mm (95% CI: 839.4 ± 122.8 under SSP5-8.5). With a CV of 26–37%, spring falls into the highly variable category. This unpredictability threatens germination and planting decisions, as farmers cannot rely on stable rainfall onset to guide cropping calendars.
Summer, the main growing season, is somewhat less variable but still poses risks. For 2026–2050, rainfall is projected at 245.4–719.8 mm (95% CI: 424.7 ± 44.1 under SSP2-4.5) and 256.0–752.0 mm (95% CI: 443.9 ± 46.1 under SSP5-8.5). By 2051–2075, projections increase to 265.1–776.0 mm (95% CI: 467.0 ± 48.1 under SSP2-4.5) and 307.9–902.4 mm (95% CI: 553.1 ± 56.5 under SSP5-8.5). Although the summer CV (~26%) is lower than in winter and spring, inter-annual fluctuations remain substantial. Variability during this critical growth stage could lead to both water stress and flooding, with severe implications for crop yields, livestock feed, and soil fertility.
Across all seasons, variability is consistently greater under the high-emission scenario (SSP5-8.5), reflecting the intensified hydrological cycle that amplifies both wet and dry extremes. This is consistent with recent studies across Ethiopia and Sub-Saharan Africa, which demonstrate that climate change is driving increased rainfall irregularity and inter-annual fluctuations rather than smooth directional shifts [65–69]. For farmers, the implications are spells or false planting signals, crop failure from unexpected dry spells, or yield losses due to flooding. Irregular planting and harvesting windows also heighten post-harvest losses and disease risks [65,70]. While the use of CMIP6 ensemble models enhances reliability, uncertainties remain due to internal climate variability, structural differences among models, and scenario assumptions [2].
Nevertheless, the consistent multi-model signal is that rainfall in DLWZ will become increasingly unpredictable and extreme. The defining feature of future rainfall will not be whether it increases or decreases, but rather how irregular and volatile it becomes across seasons. This escalating variability undermines the reliability of rainfed agriculture and heightens exposure to both drought and flood risks. Adaptation strategies must therefore prioritize flexibility, with a focus on livelihood diversification and management practices that anticipate extreme seasonal swings in rainfall.
3.5. Projected rainfall anomaly in drylands of Wolaita Zone
Projected rainfall anomalies in the drylands of Wolaita Zone (DLWZ) reveal pronounced spatial and temporal variability under both SSP2-4.5 and SSP5-8.5 scenarios. In the near-term period (2026–2050), annual rainfall is projected to change by –5.2 to 26.5% (95% CI: 10.3 ± 3.2) under SSP2-4.5, and by –2.6 to 29.0% (95% CI: 13.1 ± 3.2) under SSP5-8.5. By mid-century (2051–2075), projected changes widen to –10.1 to 34.5% (95% CI: 14.1 ± 4.0) under SSP2-4.5 and –0.3 to 43.0% (95% CI: 23.9 ± 3.9) under SSP5-8.5 (S3 Data). This pattern of overall growth coupled with extreme interannual fluctuations highlights the limitations of relying on mean values alone for agricultural planning and water resource management.
Seasonal projections show highly heterogeneous shifts. Winter rainfall is expected to increase in most years (Fig 6A, 6B). During 2026–2050, anomalies range from –31.4 to 53.5% (95% CI: 3.3 ± 8.4) under SSP2-4.5, and –26.9 to 56.0% (95% CI: 8.3 ± 8.3) under SSP5-8.5. By 2051–2075, projections expand to –28.4 to 54.9% (95% CI: 7.8 ± 8.5) under SSP2-4.5, and –14.7 to 63.9% (95% CI: 22.0 ± 8.2) under SSP5-8.5 (S3 Data). Spring rainfall is projected to increase substantially (Fig 6C, 6D). Between 2026 and 2050, anomalies range from 7.4 to 50.1% (95% CI: 24.9 ± 4.5) under SSP2-4.5, and 9.1 to 51.6% (95% CI: 27.1 ± 4.5) under SSP5-8.5. By 2051–2075, projections range from –17.7 to 51.4% (95% CI: 25.6 ± 7.7) under SSP2-4.5, and –6.8 to 57.8% (95% CI: 34.0 ± 7.4) under SSP5-8.5. While increased spring rainfall may support crop germination and early growth, irregular distribution could heighten runoff and soil erosion risks. In contrast, summer rainfall shows complex and often opposing signals (Fig 6E, 6F). For 2026–2050, anomalies range from –31.8 to 20.6% (95% CI: –7.0 ± 5.0) under SSP2-4.5, and –29.8 to 22.7% (95% CI: –4.8 ± 5.0) under SSP5-8.5. By 2051–2075, anomalies shift to –28.2 to 24.2% (95% CI: –2.3 ± 5.0) under SSP2-4.5, and –21.2 to 31.2% (95% CI: 6.1 ± 4.9) under SSP5-8.5. These seasonal divergences indicate that adaptation strategies must account not only for different emission pathways but also for distinct seasonal responses.
The anomaly is given in percent (%) in Y-axis.
Overall, SSP5-8.5 projects larger increases in annual and seasonal precipitation compared to SSP2-4.5, consistent with the intensification of the hydrologic cycle under higher greenhouse gas emissions. However, the benefits of greater rainfall are likely offset by irregular temporal distribution and increased evapotranspiration driven by rising temperatures. National-scale assessments similarly report steady increases in Ethiopia’s annual precipitation under CMIP6 scenarios [71], while large-scale hydro-climatic studies highlight rising drought risks and strong spatial heterogeneity [72]. At the local scale, studies in Wolaita Zone show increasing annual and summer rainfall in highlands but declining short rains (spring) in lowland areas, emphasizing the need for location-specific planning [73]. These anomalies carry important implications for agriculture and water management. The mismatch between springtime surpluses and summertime deficits presents a major challenge for farmers relying on predictable seasonal rainfall. For example, while increased spring rainfall may benefit early crop establishment, reduced summer rainfall threatens yields of staple crops dependent on summer moisture. Adaptive strategies such as rainwater harvesting, small-scale irrigation, and soil conservation practices will be crucial to buffer both excesses and deficits. Moreover, global evidence indicates that rainfall variability could reduce maize productivity by up to 30% by 2050, potentially raising global maize prices by nearly 50% [74,75]. This poses significant risks for Ethiopia, where maize remains a principal staple.
3.6. Future rainfall trend projection in drylands of Wolaita Zone
Projections for the mid-term period (2026–2050) suggest a slight but non-significant decreasing trend in mean annual rainfall across the drylands of Wolaita Zone (DLWZ). However, by the longer-term horizon (2051–2075), both SSP2-4.5 and SSP5-8.5 scenarios show a statistically significant decline in annual rainfall, indicating that rainfall reductions may intensify as climate change progresses (Table 3). Among the seasons, the most pronounced decrease is projected during the spring, which is particularly concerning because spring rains are crucial for crop establishment and early growth. In contrast, summer and winter rainfall are projected to increase, though these increases are mostly non-significant and therefore of limited reliability. Moreover, winter rainfall, even if higher, may not bring tangible agricultural benefits without the necessary infrastructure for irrigation or water harvesting, as it often contributes more to runoff than to soil moisture recharge.
These projected downward trends, especially in spring rainfall, have serious implications for agriculture in the DLWZ. Reduced rainfall during the planting season may directly lower yields of rain-fed crops, exacerbate water scarcity, and undermine food security. Local farmers are particularly vulnerable given the limited availability of adaptation technologies, financial resources, and institutional support. The findings in DLWZ align with broader national assessments showing that Ethiopian agriculture is highly sensitive to shifts in rainfall patterns. This would have a projected decline in net crop yields per hectare by mid- and late century under climate change scenarios [76]. The convergence of local and national evidence indicates that climate change represents a widespread threat to agricultural sustainability across Ethiopia.
The need for adaptation is therefore urgent. Early sowing of seeds, as recommended by recent studies [77], can help synchronize crop cycles with shifting rainfall patterns, reducing the risks of poor establishment and shortened growth periods. In addition, the development and use of drought-resistant crop varieties, adoption of improved water management techniques such as rainwater harvesting and small-scale irrigation, and greater access to timely weather and climate information are critical measures for enhancing resilience. Recent studies also highlight that rainfall changes are highly spatially heterogeneous across agro-ecological zones (AEZs) of Wolaita, with lowland stations generally showing weakly positive trends while midland and highland zones display mixed tendencies [78]. Broader analyses of southern Ethiopia further increase in mean annual precipitation in the second half of the century—rising by about 37% during 2041–2070 and by more than 52% during 2071–2100 [79].
National climate assessments under CMIP6 models similarly indicate long-term gains in rainfall, although the benefits may be offset by seasonal deficits and higher evapotranspiration due to rising temperatures [72]. Taken together, these projections suggest that while Ethiopia in general may experience wetter conditions in the future, DLWZ faces the distinct risk of declining rainfall in critical seasons such as spring, with potentially severe consequences for local agriculture and livelihoods. Without targeted adaptation strategies, these trends could aggravate food insecurity, reduce farm incomes, and limit the resilience of already vulnerable communities.
4. Study limitations
Although this study offers important evidence on rainfall variability and change in the drylands of Wolaita Zone, a few considerations remain. The rainfall records (1990–2022) provide a relatively long period for analysis, but they may not capture all aspects of multi-decadal climate variability. Station-based data were used, which, while reliable, may not fully reflect the spatial heterogeneity of rainfall across the zone. Projections were examined under two scenarios (SSP2-4.5 and SSP5-8.5), and although bias correction improves their performance, some uncertainties inherent to climate models and downscaling methods remain, particularly in representing localized convective rainfall. The focus of the study was limited to rainfall, without incorporating related climate variables such as temperature, evapotranspiration, rainfall onset and cessation trends, or soil moisture, which could provide a more comprehensive view of climate impacts. Likewise, while the findings highlight agricultural risks, direct quantification of effects on crop yields or food security was beyond the scope of this analysis. Finally, long-term projections (2051–2075) should be interpreted with some caution, as uncertainty generally increases with longer lead times. Despite these considerations, the study provides a strong and context-specific basis for understanding rainfall dynamics in southern Ethiopia and serves as a valuable reference for future research on climate impacts and adaptation.
5. Policy implications
The findings of this study have several policy implications for climate adaptation and agricultural development in the drylands of Wolaita Zone. The observed and projected high rainfall variability highlights the need for policies that strengthen climate-resilient agriculture, including the promotion of drought-tolerant crop varieties, improved livestock management, and integrated soil and water conservation practices. The projected decline in spring rainfall, a critical season for planting, calls for adjustments in cropping calendars, expanded use of irrigation, and water harvesting technologies to reduce production risks. Strengthening early warning and climate information services is also essential to help farmers make timely decisions in response to rainfall variability. Moreover, long-term projections suggest increasing rainfall in summer and winter, which could provide opportunities for strategic diversification of crops and improved pasture development, if supported by proper planning. Policymakers should also prioritize investment in rural infrastructure, including small-scale irrigation schemes, storage facilities, and market access, to enhance the resilience of smallholder farmers. Finally, integrating rainfall variability analysis into regional development planning and food security strategies will be critical for reducing vulnerability and ensuring sustainable livelihoods under a changing climate.
6. Conclusion
Summer and spring are the primary rainy seasons in the drylands of Wolaita Zone (DLWZ), together contributing the lion share to the annual rainfall. Among them, summer alone provides nearly half of the yearly total, underscoring its critical role in sustaining agricultural production. This seasonal rainfall distribution mirrors national patterns observed across Ethiopia’s diverse agro-ecological zones, reinforcing the need for all climate adaptation and planning efforts to be anchored around these two seasons. Over the past 33 years (1990–2022), rainfall in DLWZ has been highly to extremely variable across seasonal and annual timescales. Most seasons have shown a declining trend in rainfall, except for winter, which indicated a non-significant increase. Such variability poses significant challenges to smallholder farming systems that depend on the timely and predictable onset of rainfall for planting and production.
In terms of future projections, the multiplicative delta change (MDC) method proved to be the most effective bias correction approach for daily rainfall estimates. MDC improves the ensemble mean performance by 62–80% compared to individual climate models. Interestingly, the MPI-ESM1–2-HR model even outperformed the ensemble mean after correction, highlighting the importance of model-specific strengths. Seasonal rainfall is projected to remain highly to extremely variable, while annual totals may show medium to high variability. Under both SSP2-4.5 and SSP5-8.5 scenarios, spring rainfall is expected to decline sharply, while summer and winter rainfall are projected to increase—winter markedly so after mid-century (2051–2075). These projected declines in spring rainfall, combined with past reductions in spring and summer precipitation, are likely to disrupt planting cycles, reduce crop yields, and heighten food insecurity in already climate-vulnerable communities.
The findings point to several policy implications. First, adaptation planning at both local and national levels should explicitly prioritize spring and summer rains, given their overwhelming importance for agriculture. Second, anticipatory measures such as investment in early warning systems, drought-tolerant crops, water harvesting technologies, and adaptive land-use planning will be crucial to safeguard food security and livelihoods in the region. Third, opportunities arising from projected increases in winter and summer rainfall could be strategically leveraged through crop diversification, improved pasture management, and irrigation planning.
Equally important, the study highlights critical avenues for future research. Further work is needed to directly link seasonal rainfall changes with crop yields, livestock productivity, and household food security under different climate scenarios. The daily rainfall dataset developed here provides a valuable foundation for such modeling and impact assessments, which could inform more precise and locally tailored interventions. Expanding the integration of climate projections with socioeconomic and land-use data will also strengthen the evidence base for climate-resilient agricultural policies in the drylands of southern Ethiopia.
Supporting information
S1 Data. XL Sheets Winter, Spring, and Summer.
Rainfall anomaly and its confidence intervals of winter, spring, and summer seasons, respectively, under SSP2-4.5 and SSP5-8.5 scenarios for 2026–2050.
https://doi.org/10.1371/journal.pclm.0000800.s001
(XLSX)
S3 Data. XL Sheets 2026 and 2051.
Annual rainfall anomaly and its confidence intervals in 2026–2050, and 2051–2075, respectively, under SSP2-4.5 and SSP5-8.5 scenarios.
https://doi.org/10.1371/journal.pclm.0000800.s003
(XLSX)
References
- 1.
FAO. The State of Food Security and Nutrition in the World 2018: Building climate resilience for food security and nutrition. Rome: FAO; 2018.
- 2.
IPCC. Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press; 2021.
- 3.
U.S. Environmental Protection Agency (EPA). Climate Change Indicators: Precipitation. Washington, DC: EPA; 2022.
- 4. Giorgi F, Gutowski WJ Jr. Regional Dynamical Downscaling and the CORDEX Initiative. Annu Rev Environ Resour. 2015;40(1):467–90.
- 5.
IPCC. Climate Change 2014: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press; 2014.
- 6. Maraun D, Shepherd TG, Widmann M, Zappa G, Walton D, Gutiérrez JM, et al. Towards process-informed bias correction of climate change simulations. Nature Clim Change. 2017;7(11):764–73.
- 7.
Wilby RL, Fowler HJ. Regional climate downscaling. In: Encyclopedia of Hydrological Sciences. Wiley; 2011.
- 8. Teutschbein C, Seibert J. Bias correction of regional climate model simulations for hydrological climate-change impact studies: Review and evaluation of different methods. J Hydrol. 2012;456–457:12–29.
- 9. Rockström J, Falkenmark M, Lannerstad M, Karlberg L. The planetary water drama: Dual task of feeding humanity and curbing climate change. Geophys Res Lett. 2012;39(15):L01402.
- 10. Nicholson SE. Climate and climatic variability of rainfall over eastern Africa. Rev Geophys. 2017;55(3):590–635.
- 11. Conway D, Schipper ELF. Adaptation to climate change in Africa: Challenges and opportunities identified from Ethiopia. Global Environ Change. 2011;21(1):227–37.
- 12. Williams AP, Funk C. A westward extension of the warm pool leads to a westward extension of the Walker circulation, drying eastern Africa. Clim Dyn. 2011;37(11–12):2417–35.
- 13. Dunning CM, Black E, Allan RP. Later Wet Seasons with More Intense Rainfall over Africa under Future Climate Change. J Climate. 2018;31(23):9719–38.
- 14. Seleshi Y, Camberlin P. Recent changes in dry spell and extreme rainfall events in Ethiopia. Theor Appl Climatol. 2006;83(1–4):181–91.
- 15. Cheung WH, Senay GB, Singh A. Trends and spatial distribution of annual and seasonal rainfall in Ethiopia. Int J Climatol. 2008;28(13):1723–34.
- 16. Gebrehiwot T, van der Veen A. Assessing the evidence of climate variability in the northern part of Ethiopia. J Develop Agric Econ. 2013;5(3): 104–19.
- 17. Woldemeskel FM, Conway D. Observed and future changes in rainfall variability and extremes over southern Ethiopia. Int J Climatol. 2017;37(12): 4367–78.
- 18. Taye MT, Willems P. Influence of climate variability on representative stations for trend detection in southern Ethiopia. Hydrol Earth Syst Sci. 2013;17(6):2451–60.
- 19. Kebede M, Teferi E. Contrasting hydro-climatic trends and drought dynamics in Ethiopia and South Africa under climate change. Climate Dynamics. 2025;63:Article 105.
- 20. Tesfaye A, Bekele L. Future changes in precipitation extremes over East Africa based on CMIP6 models. Water. 2023;13(17): 2358.
- 21. Girma H, Alemu G. Alterations in hydrological responses in the Upper Blue Nile Basin under SSP scenarios. Water. 2023;16(7):1037.
- 22. EMA. Ethiopia Mapping Agency. 2025. Available from: https://www.arcgis.com/home/group.html?id=2ba2d0a030cf42c785804ab5023759f6#overview
- 23. NMI. National meteorological agency of Federal Democratic Republic of Ethiopia, Addis Ababa, Ethiopia. 2023. Available from: http://www.ethiomet.gov.et
- 24. IGRAC. International Groundwater Resources Assessment Centre: Geological map of Ethiopia (Africa Groundwater Atlas, 2019). 2020. Available from: https://ggis.un-igrac.org/layers/BGS_groundwater:bgs_geol_ethiopia
- 25. Elias E. Characteristics of Nitisol profiles as affected by land use type and slope class in some Ethiopian highlands. Environ Syst Res. 2017;6(1).
- 26. Kassa Cholbe M, Kebede Yeme F, Haile Woldeyohannes W. Fertility Status of Acid Soils under Different Land Use Types in Wolaita Zone, Southern Ethiopia. Appl Environ Soil Sci. 2020;2020:1–9.
- 27. Esayas B. Vulnerability to the changing climate and quest for livelihood resilience: Agroe-cology based analysis in Wolaita Zone, Southern Ethiopia. 2019. Available from: https://etd.aau.edu.et/server/api/core/bitstreams/41f97a4d-bd1c-499f-9053-1fc7787ff413/content
- 28. Aboye AB, Kinsella J, Mega TL. Farm households’ adaptive strategies in response to climate change in lowlands of southern Ethiopia. Int J Clim Change Strat Manag. 2023;15(5):579–98.
- 29. Aboye AB, Kinsella J, Leza T. Major climatic changes experienced by farm households: Evidence from the lowlands of Southern Ethiopia. Adv Ag Dev. 2022;3(1):87–102.
- 30. Dalle D, Gecho Y, Belay Bedeke S. Navigating dynamics of farm household livelihood vulnerability to climate-related risks in southern Ethiopia: An agroecology-based comparative analysis. Cogent Food Agri. 2023;10(1).
- 31. Mengistu M, Tebeje M, Balta A, Debala D, Limani B. Exploring potential adaptation strategies for smallholder farmers to climate change. Trend Ecol Indoor Environ Eng, 2024;2(3):26–34.
- 32. Tessema MW, Atnafe AD, Tora SE, Bantider A, Shure E. Trend analysis of climate change and effect of climate-smart agricultural practices: the case of Humbo Woreda, Wolaita Zone, South Ethiopia. Front Clim. 2025;7.
- 33. Hamzah FB, Mohd Hamzah F, Mohd Razali SF, Samad H. A Comparison of Multiple Imputation Methods for Recovering Missing Data in Hydrological Studies. Civ Eng J. 2021;7(9):1608–19.
- 34. Sattari M-T, Rezazadeh-Joudi A, Kusiak A. Assessment of different methods for estimation of missing data in precipitation studies. Hydrol Res. 2016;48(4):1032–44.
- 35. Chinasho A, Bedadi B, Lemma T, Tana T, Hordofa T, Elias B. Evaluation of Seven Gap-Filling Techniques for Daily Station-Based Rainfall Datasets in South Ethiopia. Adv Meteorol. 2021;2021:1–15.
- 36. Tsidu GM. High-Resolution Monthly Rainfall Database for Ethiopia: Homogenization, Reconstruction, and Gridding. J Clim. 2012;25(24):8422–43.
- 37. Jalili Pirani F, Modarres R. Geostatistical and deterministic methods for rainfall interpolation in the Zayandeh Rud basin, Iran. Hydrol Sci J. 2020;65(16):2678–92.
- 38. ICPAC (IGAD Climate Prediction and Applications Centre). Evaluation of CMIP6 historical simulations over the IGAD region of Eastern Africa (1981–2014). ICPAC Technical Report. 2023. Available from: https://icpac.net/
- 39. Schwarzwald K, Tierney JE, Nicholson SE, et al. Revisiting the “East African Paradox”: CMIP6 models and rainfall projections over East Africa. J Clim. 2024;37(12):4567–84.
- 40. Brunner L, Pendergrass AG, Lehner F, Merrifield AL, Lorenz R, Knutti R. Reduced global warming from CMIP6 projections when weighting models by performance and independence. Earth Syst Dynam. 2020;11(4):995–1012.
- 41. Talukder A, Zaman M, Saha A, et al. Optimizing the multi-model ensemble of CMIP6 GCMs for improved climate projections. Sci Rep. 2025;15: 4562.
- 42. Mekonnen MA, Taye MT, Zeleke TT, et al. Performance evaluation of CMIP6 climate model simulations for precipitation and temperature over Ethiopian river basins. Climate. 2022;10(9):125.
- 43. Rettie FM, Robertson AW, Hanemann WM, et al. High-resolution CMIP6 climate projections for Ethiopia using statistical downscaling and bias correction. Scientific Data. 2023;10: 265.
- 44. Ezéchiel O, Eric AA, Josué ZE, Eliézer BI, Amédée C. Comparative study of seven bias correction methods applied to three Regional Climate Models in Mekrou catchment (Benin, West Africa). Int J Curr Eng Technol. 2016;6(5):1831–40.
- 45. Mendez M, Maathuis B, Hein-Griggs D, Alvarado-Gamboa L-F. Performance Evaluation of Bias Correction Methods for Climate Change Monthly Precipitation Projections over Costa Rica. Water. 2020;12(2):482.
- 46. Mengistu AG, Woldesenbet TA, Dile YT. Evaluation of the performance of bias-corrected CORDEX regional climate models in reproducing Baro–Akobo basin climate. Theor Appl Climatol. 2021;144(1–2):751–67.
- 47. Ware MB, Matewos T, Guye M, Legesse A, Mohammed Y. Spatiotemporal variability and trend of rainfall and temperature in Sidama Regional State, Ethiopia. Theor Appl Climatol. 2023;153(1–2):213–26.
- 48. Kerebo KA, Bizuneh YK, Mekonnen AG, Mohammed Y. The trends and spatiotemporal variability of temperature and rainfall in Hulbarag district, Silte Zone, Ethiopia. Heliyon. 2024;10(11):e31646. pmid:38841449
- 49. Gomes FP. Course in experimental statistics. 1985. Available from: https://repositorio.usp.br/item/000773318
- 50. Mann HB. Nonparametric Tests Against Trend. Econometrica. 1945;13(3):245.
- 51. Kendall MG. Rank correlation methods. 1975. Available from: https://search.worldcat.org/title/Rank-correlation-methods/oclc/3827024
- 52.
R Core Team. R: A language and environment for statistical computing: R foundation for statistical computing. Vienna, Austria. 2021. Available from: https://www.R-project.org/
- 53. Findlater J. Interhemispheric transport of air in the lower troposphere over the western Indian Ocean. Quarter J Roy Meteor Soc. 1969;95(404):400–3.
- 54. Kinuthia JH, Asnani GC. A Newly Found Jet in North Kenya (Turkana Channel). Mon Wea Rev. 1982;110(11):1722–8.
- 55. King JA. Variability of the Turkana low-level jet in reanalysis and models: Implications for rainfall. J Geophys Res Atmos. 2021;126(7): e2020JD034348.
- 56. Segele ZT, Lamb PJ. Characterization and variability of Kiremt rainy season over Ethiopia. Monthly Weather Rev. 2005;133(6):1169–88.
- 57. Misiani HO, Finney DL, Segele ZT, Marsham JH, Tadege A, Artan G, et al. Circulation Patterns Associated with Current and Future Rainfall over Ethiopia and South Sudan from a Convection-Permitting Model. Atmosphere. 2020;11(12):1352.
- 58. Birara H, Pandey RP, Mishra SK. Trend and variability analysis of rainfall and temperature in the Tana basin region, Ethiopia. J Water Clim Change. 2018;9(3):555–69.
- 59. Abebe W, Fanta A, Esayas B. Rainfall variability and trends in eastern and western Harerghe Zones, Ethiopia. Environ Syst Res. 2021;10(1):1–16.
- 60. Habte A, Worku W, Mamo G, Ayalew D, Gayler S. Rainfall variability and its seasonal events with associated risks for rainfed crop production in Southwest Ethiopia. Cogent Food Agric. 2023;9(1):2231693.
- 61. Debisa DD, Bayu TY, Tora TT. Spatio-temporal trends in extreme climatic variables: a focus in southern Ethiopia. Discov Appl Sci. 2025;7(6):638.
- 62. Asaye TS, Amare ZY, Geremew BB, Abebe MG. Rainfall variability and trends in western Amhara: implication for sustainable water management and agricultural productivity. Water Pract Technol. 2025;20(2):449–67.
- 63. Palmer PI, Wainwright CM, Dong B, Maidment RI, Wheeler KG, Gedney N, et al. Drivers and impacts of Eastern African rainfall variability. Nat Rev Earth Environ. 2023;4(4):254–70.
- 64. Demissew WL, Zeleke TT, Ture K, Mengistu DK, Fufa MA. Agroclimatic Indicator Analysis Under Climate Change Conditions to Predict the Climatic Suitability for Wheat Production in the Upper Blue Nile Basin, Ethiopia. Agriculture. 2025;15(5):525.
- 65. Gobie BG, Asamnew AD, Habtemichael BA. Projected annual precipitation trend in Ethiopia under CMIP6 models in the 21st century. Model Earth Syst Environ. 2024;10(3):3633–44.
- 66. Gashaw T, Worqlul AW, Taye MT, Lakew HB, Seid A, Ayele G, et al. Performance evaluations of CMIP6 model simulations and future projections of rainfall and temperature in the Bale Eco-Region, Southern Ethiopia. Theor Appl Climatol. 2024;155(6):5069–92.
- 67. Berhanu D, Alamirew T, Taye MT, Tibebe D, Gebrehiwot S, Zeleke G. Evaluation of CMIP6 models in reproducing observed rainfall over Ethiopia. J Water Clim Change. 2023;14(8):2583–605.
- 68. Ayugi J, Alaminie T, Deme I, et al. Evaluation and projection of CMIP6 simulations of climate variables for the Rift Valley Lakes Basin, Ethiopia. Theor Appl Climatol. 2025.
- 69. Diro GT, Mekonnen A, Alemayehu A, et al. Rainfall variability and extremes over Eastern Africa in CMIP6 simulations. Climate Dynamics. 2024.
- 70. Thornton PK, Ericksen PJ, Herrero M, Challinor AJ. Climate variability and vulnerability to climate change: a review. Glob Chang Biol. 2014;20(11):3313–28. pmid:24668802
- 71. Gobie WG, Melesse AM, Ayalew S, et al. Future rainfall and temperature projections under CMIP6 scenarios in Ethiopia. Environ Earth Sci. 2024;83:1971.
- 72. Mengistu D, Ayenew T, et al. Hydro-climatic changes and extremes over Ethiopia under CMIP6 projections. Climate Dynamics. 2025.
- 73. Arba A. Spatio-Temporal Variability and Trends of Annual and Seasonal Rainfall in Wolaita Zone, Ethiopia. AJWSE. 2025;11(2):16–23.
- 74. Ray DK, West PC, Clark M, Gerber JS, Prishchepov AV, Chatterjee S. Climate change has likely already affected global food production. PLoS One. 2019;14(5):e0217148. pmid:31150427
- 75. Tigchelaar M, Battisti DS, Naylor RL, Ray DK. Future warming increases probability of globally synchronized maize production shocks. Nature Clim Change. 2018;8:221–5.
- 76. Gebrechorkos SH, Hülsmann S, Bernhofer C. Statistically downscaled climate scenarios for East Africa: future trends in rainfall and temperature. Climate. 2019;7(10):1–25.
- 77. Admassu H, Belay S, Mengistu D. Climate change adaptation through early planting: opportunities and challenges for smallholder farmers in Ethiopia. Agric Syst. 2023;206:103637.
- 78. Belayneh A, Arba G, Feleke Z. Spatiotemporal trends of rainfall across agro-ecological zones of Wolaita, Southern Ethiopia. Heliyon. 2024;10(5): e29031.
- 79. Dinku T, Mengistu D, et al. Future hydro-climatic projections for southern Ethiopia under CMIP6 scenarios. SN Appl Sci. 2025;7:219.