Climate Model Analysis of Extreme Precipitation Clustering and Flood Risk in Europe

Extreme precipitation plays a key role in flood risk management in Europe, where multi-day heavy rainfall often causes severe impacts. This study assessed how well climate models reproduce both the intensity and the temporal distribution of extreme events using daily data from 1979 to 2023. Observations from the E-OBS dataset were compared with outputs from CMIP6 models, with a focus on maximum consecutive wet days and clustering of events. The results show that median spatial correlations between simulated and observed extreme-day counts were about 0.6–0.7, but models often underestimated the duration of wet spells and misrepresented their order, especially in mountainous and coastal areas. Regression analysis and Kling–Gupta Efficiency values pointed to biases related to convection and microphysics schemes. The results indicate that models reproduce large-scale patterns but do not represent storm duration and clustering with enough accuracy. This work shows the need to improve model physics and post-processing methods to better capture the temporal structure of extreme precipitation and to support practical use in flood prediction and water management.