Present and future trends of extreme short-term rainfall events in Germany, by downscaling convective environments of ERA5 and a CMIP6 ensemble

Abstract. For the four main quadrant regions of Germany we study the possibility of projecting the occurrence of extreme convective rainfall events, as monitored by the CatRaRE database, into the future, based on CMIP6 projections of corresponding convective environments. We characterize such environments by using the atmospheric profile derivates convective available potential energy (cape) and convective inhibition (cin), along with model-simulated convective precipitation (cp). The convective environments are linked to the small-scale CatRaRE events by classifying the corresponding ERA5 fields according to the concurrent occurrence of such events. Classifiers are conventional machine-learning procedures along with more modern deep learning schemes. Positive centennial trends are identified for both antagonists cape and cin, serving as a source for large uncertainties of the corresponding CatRaRE-type trends. Their full distribution is analyzed using ANOVA, based on the factors of event severity, greenhouse gas emissions, climate models, classifiers, and region. Beyond all uncertainty, positive trends outweigh the negative ones for all regions.