Understanding the Biases in Daily Extreme Precipitation Climatology in CMIP6 Models

Abstract

Future projections in extreme precipitation depend heavily on climate models. Therefore, assessing their fidelity in reproducing the extreme rainfall characteristics in historical simulation is critical. We evaluated CMIP6 models' performance in reproducing the climatology of daily extremes, focusing on the global land monsoon (GLM) domain that feeds two‐thirds of the world's population. Compared with ERA5, models demonstrate a significant wet bias in GLM domain for the annual maximum daily precipitation (14.14%) and the extreme tail of daily precipitation distributions (32.53%), more than twice the global average. Decomposition of biases reveals that dynamic processes, particularly vertical velocity, primarily drive these biases. Using the quasi‐geostrophic $$\omega $$ equation, we determined that the component associated with large‐scale adiabatic disturbances ($${\omega }_{D}$$) mainly drives vertical velocity biases, with diabatic heating term amplifying them. Furthermore, a significant correlation between $${\omega }_{D}$$ biases and baroclinicity biases in midlatitude suggests that baroclinicity biases are a key contributor to the vertical velocity biases.