Spatiotemporal trends in temperature and rainfall within the coffea arabica landscapes of Gimbo and Decha districts in the Kafa Zone, Southwest Ethiopia

Abstract Climate change poses escalating risks to rain-fed agricultural systems, particularly for Coffea arabica, a perennial crop with narrow bioclimatic requirements that supports the livelihoods and ecological heritage of Ethiopia. This study evaluates spatiotemporal trends in temperature and rainfall across the Gimbo and Decha districts from 1990 to 2022, with the specific objectives of characterizing shifts in thermal regimes, assessing rainfall distribution and concentration, and mapping the spatial heterogeneity of climatic trends. Historical records from ten meteorological stations were integrated with CHIRPS and ERA5-Land datasets, and analyzed using the Mann-Kendall test, Sen’s slope estimator, Innovative Trend Analysis, Precipitation Concentration Index, and regression kriging for spatial interpolation. Results reveal a coherent warming signal with annual mean temperatures increasing by +0.02°C year⁻ 1 (p < 0.001). Maximum temperature rise significantly during the dry months (January-March), while minimum temperature increased notably during the main rainy season (June-July), suggesting a gradual compression of the diurnal temperature range. Annual rainfall exhibited a modest upward trend (+2.69 mm year⁻ 1 , p = 0.016), yet seasonal precipitation is highly concentrated (PCI > 20 in 75.8% of Kiremt and 61.8% of Belg) and marked by noticeable interannual irregularity, including sharp oscillations between extreme dry and wet periods. Spatial interpolation highlighted topographically mediated microclimatic gradients, with warmer southern parts contrasting with cooler northern and eastern zones. Collectively, these patterns indicate a shifting environmental baseline characterized by escalating thermal stress and hydro-climatic unpredictability, which may progressively challenge coffee phenological synchronization and yield stability. The study forms a spatially explicit, ground-validated climatic baseline data that can inform targeted, landscape-level adaptation strategies, and serve as a foundational dataset for integrating process-based crop models and climate-resilient planning in this highly significant coffee-producing region.