Projected changes in rainfall over Uganda based on CMIP6 models

Abstract

Information about likely future patterns of climate variables is important in climate change mitigation and adaptation efforts. This study investigates future (2021–2100) changes in rainfall based on CMIP6 datasets over Uganda. The projection period is divided into two sub-periods: 2021–2060 (near future) and 2061–2100 (far future), relative to the baseline period (1985–2014). Two emission scenarios: SSP2-4.5 and SSP5-8.5, are considered. The results reveal a larger decrease (increase) in rainfall during March–April (November–December) under both SSPs. Moreover, an enhanced decline (increase) is projected under SSP2-4.5 (SSP5-8.5). The spatial distribution of future changes in seasonal rainfall reveals a decrease in MAM rainfall in the near future over most parts of the country under both emission scenarios. However, a recovery is exhibited towards the end of the century with more increase in the south-western parts of the country, and a higher magnitude under SSP5-8.5. In contrast, SON rainfall reveals wetter conditions during both timelines and emission scenarios. Maximum (minimum) wet conditions are expected in the north-western parts of the country (around the Lake Victoria basin). The linear trend analysis shows a non-significant (z = − 0.714) decreasing trend for MAM rainfall during the historical period. This pattern is reflected in the near future with z-scores of − 0.757 and − 1.281 under SSP2-4.5 and SSP5-8.5, respectively. However, a significant increase for MAM and annual rainfall (z-scores of 2.785 and 3.46, respectively) is projected towards the end of the century under SSP5-8.5. These findings provide guidance to policy makers in devising appropriate adaptation measures to cope with expected changes in the local climate. Given the increase in intensity and frequency of extreme rainfall over the study region, future work should focus on examining projected changes in rainfall extremes under different global warming scenarios with consideration of model performance and independence.