Strong intermodel differences and biases in CMIP6 simulations of PM _2.5 , aerosol optical depth, and precipitation over Africa

Abstract. Poor air quality and precipitation change are strong, rapidly changing, and possibly linked drivers of physical hazards in sub-Saharan Africa. Future projections of sub-Saharan air quality and precipitation remain uncertain due to differences in model representations of aerosol, aerosol–precipitation interactions, and unclear future aerosol emission pathways. In this study, we evaluate the performance of CMIP6 models in simulating PM2.5, aerosol optical depth (AOD), and precipitation over Africa relative to a range of observational and reanalysis products, including novel observational datasets, over the 1981–2023 period. While models accurately capture the seasonal cycle of PM2.5 concentrations over most regions, the concentration magnitudes show strong intermodel diversity. Dust AOD shows a generally accurate seasonal spatial distribution, with multi-model mean (MMM) pattern correlation coefficients within 0.77–0.94, despite strong intermodel diversity in magnitude. Seasonal spatial patterns of non-dust AOD are poorly represented, with MMM pattern correlation coefficients of 0.25–0.58 and the poorest performance during September through November. Emission inventory inaccuracies may explain systematic biases for non-dust AOD fields, with differences in circulation and precipitation patterns, as well as aerosol treatment causing intermodel diversity. The magnitude and annual progression of precipitation over both the east and west African monsoon regions are well captured, though there is poorer performance in simulating the east African monsoon. Biases found relate to the intertropical convergence zone, more apparent over east Africa, and rainfall magnitude, more apparent over west Africa. This evaluation highlights strong intermodel diversity in the representation of African air quality and climate and identifies model performance over sub-Saharan Africa and the reasons behind the biases as critical gaps to address for improving confidence in climate projections.