The research team focused on the regions dominated by Coffea arabica Linnaeus, the species that underpins the livelihoods of millions of smallholder farmers across the Ethiopian highlands. To generate projections at a resolution useful for agricultural planning, the scientists applied empirical quantile mapping, a statistical bias-correction technique that adjusts the systematic errors of global climate models against observed station records and satellite-based reference datasets. The correction proved highly effective. For daily maximum and minimum temperature, the corrected ensemble achieved Nash-Sutcliffe efficiency scores of 0.92 and 0.75 respectively, with coefficients of determination of 0.96 and 0.82. For precipitation, the ensemble reached a Nash-Sutcliffe efficiency of 0.91 and an R-squared of 0.96, indicating that the downscaled simulations reproduced the historical climate of the coffee zones with remarkable fidelity before any future scenario was considered.

With the corrected ensemble validated, the researchers examined two future pathways drawn from the ScenarioMIP framework: SSP2-4.5, a moderate emissions scenario in which global society follows a middle-of-the-road trajectory, and SSP5-8.5, a high-emissions future with continued reliance on fossil fuels. The temperature signal under both scenarios was unambiguous. Maximum temperatures in the coffee regions are projected to rise by 1.01 degrees Celsius by the 2031 to 2060 period under SSP2-4.5 and by 1.37 degrees under SSP5-8.5, climbing further to 1.72 and 3.27 degrees respectively by 2071 to 2100. Minimum temperatures warm even faster, increasing by 1.26 and 1.88 degrees Celsius at mid-century and by 2.00 and 3.94 degrees by the end of the century under the two scenarios. Every one of these warming trends was statistically significant at the five percent level.

The asymmetry of this warming, with nighttime minimums rising faster than daytime maximums, carries consequences that go well beyond a simple increase in average heat. A shrinking diurnal temperature range means that coffee plants experience intensified heat stress during the night, a period when the crop would normally recover from daytime photosynthetic demands. Elevated nighttime temperatures accelerate respiration, causing the plant to burn through carbohydrates that would otherwise be allocated to bean filling, vegetative growth, and stress defense. Agronomists have long linked warm nights to reduced yield stability and degraded cup quality in Arabica, and the projections suggest that Ethiopian farmers will face exactly this pressure, particularly in the lower-elevation portions of the growing zones where baseline nighttime temperatures are already close to the physiological limits of the species.

Rainfall tells a more complicated story. On an annual basis, mean precipitation over the coffee regions is actually projected to increase, by 3.47 to 9.44 percent under SSP2-4.5 and by 18.01 to 23.74 percent under SSP5-8.5. Yet the study found these annual changes to be statistically insignificant, meaning the models cannot rule out that they arise from natural variability rather than a forced climate response. The seasonal breakdown, by contrast, exposes significant and troubling shifts. Rainfall during the June to September season, the primary rainy period known locally as Kiremt and abbreviated as JJAS, is projected to decrease significantly at mid-century under the moderate scenario. Meanwhile, rainfall during the March to May Belg season, which is critical for coffee flowering, shows a significant increase at mid-century that reverses into a significant decline by the late century under the high-emissions pathway.

This seasonal whiplash is precisely the kind of change that catches farming systems off guard. Coffee flowering in Ethiopia is triggered by the onset of the rains after a dry spell, and the synchrony of flowering determines the uniformity of the harvest. A wetter March-to-May period followed by a drier June-to-September season would disrupt the moisture supply during fruit development, the stage at which the coffee cherry demands the most water. The authors caution that the decrease in rainfall during the core rainy season points to increased intra-seasonal variability and potentially serious water stress, even as the annual totals climb. In other words, more water may arrive in the year as a whole, but it may arrive at the wrong times, in the wrong intensities, and with longer dry gaps in between, a pattern familiar from broader research on how the tropical water cycle responds to warming.

The study’s methodology reflects the current best practice in regional climate impact research. Rather than relying on a single global model, the team used a multi-model ensemble, which averages out the idiosyncratic errors of individual simulations and provides a more defensible estimate of the range of possible futures. Trend detection was carried out with the Mann-Kendall nonparametric test and Sen’s slope estimator, standard tools for identifying monotonic changes in noisy hydroclimatic records. The bias-correction approach drew on established quantile-mapping methods designed to preserve changes in both the mean and the extremes of the model distributions, and the baseline climate record was anchored by observations from the Ethiopian Meteorological Institute alongside the CHIRPS satellite rainfall product and the ERA5 reanalysis from the European Centre for Medium-Range Weather Forecasts.

The stakes for Ethiopia’s economy and cultural identity are considerable. Coffee is the country’s leading export commodity and a source of income for an estimated five million or more farming households, and the genetic diversity of wild Arabica in the Ethiopian forests represents an irreplaceable global resource. Previous research has already mapped substantial contractions in suitable Arabica habitat under warming scenarios, and studies from Tanzania and other East African producers have documented yield declines tied to rising temperatures and shifting rainfall. The new projections add a crucial layer of regional specificity, showing that the canonical coffee landscapes of southwest and western Ethiopia, including zones around Jimma, will not escape the thermal trajectory of the wider East African highlands. The combination of hotter nights, altered seasonal rainfall, and heightened variability threatens both the quantity and the quality of production in areas where alternative crops are limited.

The authors argue that their results make a compelling case for climate-resilient adaptation strategies tailored to the coffee zones. Among the options highlighted are the deployment of heat-tolerant coffee varieties, improved soil and water conservation measures that help capture and store the increasingly erratic rainfall, and shade-based agroforestry systems that buffer the microclimate of coffee plots against extreme temperatures. Shade trees, in particular, address the nighttime warming problem directly by moderating both daytime heating and nighttime radiative cooling, while also contributing organic matter and suppressing erosion on the steep highland slopes. The study, conducted under the Future Africa Research Leadership Fellowship funded by the Carnegie Corporation of New York, underscores that the window for proactive adaptation is open but narrowing. As the projections make clear, the highlands that gave the world Arabica coffee are warming on a schedule set by global emissions, and the farmers who tend those slopes will need every tool available to keep the crop, and the centuries of heritage behind it, viable through the rest of this century.

Subject of Research: Projected climate change impacts on Arabica coffee growing regions in Ethiopia using CMIP6 climate model ensembles

Article Title: Projected changes in precipitation and temperature for ethiopian coffee growing regions using CMIP6 multi-model ensembles

Article References: Belay, F., Garedew, W., Gandidzanwa, C., Oljira, A., Falola-Olasunkanmi, J. A., Agyekum, J., Nyengere, J., & Dibaba, W. T. (2026). Projected changes in precipitation and temperature for ethiopian coffee growing regions using CMIP6 multi-model ensembles. Theoretical and Applied Climatology, 157(10), Article 659. https://doi.org/10.1007/s00704-026-06601-4

Image Credits: AI Generated

DOI: 10.1007/s00704-026-06601-4

Keywords: Ethiopia, Arabica coffee, CMIP6, climate projections, precipitation, temperature, bias correction, quantile mapping, SSP scenarios, agroforestry, climate adaptation, highlands