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<strong class="journal-contentHeaderColor">Abstract.</strong> Extreme daily rainfall is an important trigger for floods in Bavaria. The dimensioning of water management structures as well as building codes is based on observational rainfall return levels. In this study, three high-resolution regional climate models (RCMs) are employed to produce 10- and 100-year daily rainfall return levels and their performance is evaluated by comparison to observational return levels. The study area is governed by different types of precipitation (stratiform, orographic, convectional) and a complex terrain, with convective precipitation also contributing to daily rainfall levels. The Canadian Regional Climate Model version 5 (CRCM5) at a 12 km spatial resolution and the Weather and Forecasting Research (WRF) model at a 5 km resolution both driven by ERA-Interim reanalysis data use parametrization schemes to simulate convection. WRF at a 1.5 km resolution driven by ERA5 reanalysis data explicitly resolves convectional processes. Applying the generalized extreme value (GEV) distribution, the CRCM5 setup can reproduce the observational 10-year return levels with an areal average bias of <span class="inline-formula">+6.6</span> % and a spatial Spearman rank correlation of <span class="inline-formula"><i>ρ</i>=0.72</span>. The higher-resolution 5 km WRF setup is found to improve the performance in terms of bias (<span class="inline-formula">+4.7</span> %) and spatial correlation (<span class="inline-formula"><i>ρ</i>=0.82</span>). However, the finer topographic details of the WRF-ERA5 return levels cannot be evaluated with the observation data because their spatial resolution is too low. Hence, this comparison shows no further improvement in the spatial correlation (<span class="inline-formula"><i>ρ</i>=0.82</span>) but a small improvement in the bias (2.7 %) compared to the 5 km resolution setup. Uncertainties due to extreme value theory are explored by employing three further approaches. Applied to the WRF-ERA5 data, the GEV distributions with a fixed shape parameter (bias is <span class="inline-formula">+2.5</span> %; <span class="inline-formula"><i>ρ</i>=0.79</span>) and the generalized Pareto (GP) distributions (bias is <span class="inline-formula">+2.9</span> %; <span class="inline-formula"><i>ρ</i>=0.81</span>) show almost equivalent results for the 10-year return period, whereas the metastatistical extreme value (MEV) distribution leads to a slight underestimation (bias is <span class="inline-formula">−7.8</span> %; <span class="inline-formula"><i>ρ</i>=0.84</span>). For the 100-year return level, however, the MEV distribution (bias is <span class="inline-formula">+2.7</span> %; <span class="inline-formula"><i>ρ</i>=0.73</span>) outperforms the GEV distribution (bias is <span class="inline-formula">+13.3</span> %; <span class="inline-formula"><i>ρ</i>=0.66</span>), the GEV distribution with fixed shape parameter (bias is <span class="inline-formula">+12.9</span> %; <span class="inline-formula"><i>ρ</i>=0.70</span>), and the GP distribution (bias is <span class="inline-formula">+11.9</span> %; <span class="inline-formula"><i>ρ</i>=0.63</span>). Hence, for applications where the return period is extrapolated, the MEV framework is recommended. From these results, it follows that high-resolution regional climate models are suitable for generating spatially homogeneous rainfall return level products. In regions with a sparse rain gauge density or low spatial representativeness of the stations due to complex topography, RCMs can support the observational data. Further, RCMs driven by global climate models with emission scenarios can project climate-change-induced alterations in rainfall return levels at regional to local scales. This can allow adjustment of structural design and, therefore, adaption to future precipitation conditions.

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