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Abstract:
Rwanda's steep elevation and complex terrain create strong local climatic contrasts that the coarse-resolution global climate models used in most existing studies cannot capture, limiting their value for adaptation planning. This report addresses that gap by applying a topographically informed, mechanistic downscaling method (CHELSA) to bias-corrected CMIP6 model data from nine global climate models, producing climate projections at 1km resolution for Rwanda under three SSP scenarios (SSP1-2.6, SSP3-7.0, SSP5-8.5) across three future periods (2030, 2055, 2080). All scenarios show substantial warming, with temperature extremes increasing faster than means and warming patterns spatially homogeneous across the country. Total annual precipitation is projected to increase, particularly in the September–January season, with a significant rise in heavy-precipitation days under medium- and high-emissions scenarios. Precipitation changes show much greater spatial variability than temperature. Meteorological drought trends remain statistically inconclusive, and comparisons with national observations suggest the models likely underestimate absolute drought risk. The report demonstrates how high-resolution data can inform localized climate risk assessments and recommends extending this approach to sectoral impact studies.
