Abstract. Accurate modelling of extreme precipitation is vital for predicting future risks and informing adaptation strategies. Here, we compare and evaluate six different extreme value statistical models for hourly to 48 h extreme precipitation in southern Germany, with a primary focus on duration-dependent Generalized Extreme Value (dGEV) distributions. To assess model performance, particularly in capturing tail behavior, we utilize the 50-member single model initial-condition large ensemble of the Canadian Regional Climate Model version 5 for the period 1980–2019. The large sample size of 2000 simulated years enables a robust sampling of extreme quantiles. Using a sub-sampling strategy with 30 to 100 years, we compare the efficacy of Bayesian methodology, in particular Bayesian hierarchical models, against frequentist models (L-moments and Maximum Likelihood Estimation – MLE) in representing the tail risk of 100-year return levels based on limited sample sizes. Hierarchical models allow us to give special emphasis on the dimensionality of the GEV shape parameter, a critical factor for tail behavior. Our findings reveal that a shape parameter varying over durations but fixed across space is beneficial for the prediction of the 100-year return level. The resulting Intensity-Duration-Frequency (IDF) curve shows the highest accuracy and smallest confidence intervals proving its robustness. Compared to the standard GEV estimated by L-moments, our proposed model can reduce the relative error of the 100-year return level from 18.1 % to 8.8 % based on a 30-year sample size. Furthermore, our analysis reveals fundamental limitations of the Anderson-Darling test for extreme value model selection, demonstrating its poor correlation with predictive skill for upper quantiles – a critical finding for climate risk applications.
Rischmuller et al. (2026) studied this question.