Evaluation and projections of extreme precipitation using a spatial extremes framework

Abstract

Extreme precipitation events are a major natural hazard and cause significant socio-economic damages. Precipitation events are spatially extended and, thus, can cause large water accumulations, which can lead to flooding events. In order to help design flood protection infrastructure, a detailed investigation of the temporal and spatial dependencies of extreme precipitation is essential. Here, we use a statistical spatial extremes framework to systematically study the historical and projected spatial-temporal characteristics of extreme precipitation in Germany. For this purpose, we use data from 10 high-resolution global climate model-regional climate model (GCM-RCM) combinations from the EURO-CORDEX initiative and derive a statistical spatial extremes precipitation model. Our results show that there are large spreads in reproducing the temporal-spatial characters of extreme precipitation. Few climate simulations can well present the temporal clustering of observed extreme precipitation in both summer and winter. In reproducing the spatial dependencies of the observations, most GCM-RCM combinations behave well in summer, while in winter most RCMs produce too many spatially localized extreme precipitation events. The derived statistical model, which accounts for both the spatial and temporal variability, performs well in representing the spatial dependency and intensity characteristics in summer. Furthermore, global warming will have a significant impact on the temporal and spatial dependencies of extreme precipitation in Germany. There will be more temporal-dependent and homogeneous extreme precipitation in summer; and more temporal-independent and localized extreme precipitation in winter. The intensity quantified by the 25-year return level of the 10 GCM-RCM combinations is increasing; with relative changes ranging from 5.33% to 53.24% in summer and from -15.38% to 32.33% in winter under RCP8.5. A future projection by our statistical spatial extremes model using projected temperature from GCM-RCM combinations as a covariate shows that the 25-year return level will increase by 3.02% under RCP2.6 and 4.16% under RCP8.5 in winter.