Modelled non-stationary kilometer-scale hourly precipitation extremes of a 100-year event under 2 GWL scenarios for Germany

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.

鉴于亚日极端降水事件对于洪涝灾害发生的重要性，量化当前及未来气候条件下此类极端事件发生的可能性，成为气候变化适应的关键环节。此类评估通常受限于以下因素：(i)缺乏足够密集且亚日降水观测数据；(ii)高分辨率对流允许区域气候模型（CPM）模拟，能够真实地反映亚日降水极端情况；(iii)统计方法，允许我们在数据有限和非定常条件（即气候变化）下，基于主要控制物理过程对极端降水回归水平进行外推。通过利用千米级小时雷达降水估计（RADKLIM）和空间分解的观测日温度数据（HYRAS-DE-TAS），以及实施一个涵盖德国全境的新颖CPM集合模型，我们从BMBF资助的RegIKlim（区域气候行动信息）倡议中的NUKLEUS项目中克服了这些限制。此外，我们引入了温度依赖的非渐近统计模型TENAX，一种将日温度作为协变量的新方法，与观测到的克劳修斯-克拉佩龙比例率相一致。这一创新成果为德国带来了具有划时代意义的每小时极端降水数据集，标志着首次考虑非定常气候条件，即+2K和+3K更暖的世界。新数据集包含千米级小时降水极端值，对应于100年一遇事件的回归水平。由于基于雷达的估计与地面观测相比存在固有的偏差，因此使用KOSTRA对降水极端值进行了基于回归水平的偏差调整。