Project: Klimawandelanpassung auf lokaler Ebene - The KARE project (https://www.fona.de/de/massnahmen/foerdermassnahmen/RegIKlim/kare.php) investigates the local impact of climate change and the associated consequences for the municipalities of the Bavarian Oberland, particularly concerning heavy rainfall events. The project aims to sensitize the towns and municipalities of Oberland to the consequences of climate change and, together with stakeholders from politics, business, and society, to develop and test practical instruments for municipal heavy rainfall risk management. The Federal Ministry of Education and Research (BMBF) funded the project and is embedded in the nationwide research initiative RegIKlim (Regional Information on Climate Action). Summary: Given the importance of sub-daily extreme precipitation events for the occurrence of pluvial floods, it is a key component in climate change adaptation to quantify the likelihood of such extreme events under current and future climate conditions. Such assessments are usually limited by a lack of sufficiently dense and sub-daily precipitation observations, (ii) high-resolution convection-permitting regional climate model (CPM) simulations that realistically represent sub-daily precipitation extremes, and (iii) statistical methods that allow us to extrapolate extreme precipitation return levels under limited data availability and non-stationary conditions (i.e., climate change) based on the main governing physical processes. We overcome these constraints through the utilization of kilometer-scale hourly radar precipitation estimates (RADKLIM) and spatially disaggregated observed daily temperature data (HYRAS-DE-TAS), and the implementation of a novel CPM ensemble covering the entirety of Germany, obtained from the NUKLEUS project within the BMBF-funded RegIKlim (Regionale Information zum Klimahandeln) initiative. Additionally, we introduce the Temperature-dependent Non-Asymptotic statistical model for eXtreme return levels (TENAX) model, a new approach that integrates daily temperature as a covariate, aligning with observed Clausius-Clapeyron scaling rates. This innovation results in a groundbreaking dataset of hourly extreme precipitation for Germany, marking the first instance of accounting for non-stationary climate conditions, i.e., in a +2K and +3K warmer world. The new dataset contains kilometer-scale hourly precipitation extremes for the return level of a 100-year event. Due to the inherent biases of radar-based estimates compared to ground observations, the precipitation extremes have been bias-adjusted on return level basis using KOSTRA.
