Extreme precipitation events lead to dramatic impacts on society and the situation will worsen under climate change. Decision-makers need reliable estimates of future changes as a basis for effective adaptation strategies, but projections at local scale from regional climate models (RCMs) are highly uncertain. Here we exploit the km-scale convection-permitting multi-model (CPM) ensemble, generated within the FPS Convection project, to provide new understanding of the changes in local precipitation extremes and related uncertainties over the greater Alpine region. The CPM ensemble shows a stronger increase in the fractional contribution from extreme events than the driving RCM ensemble during the summer, when convection dominates. We find that the CPM ensemble substantially reduces the model uncertainties and their contribution to the total uncertainties by more than 50%. We conclude that the more realistic representation of local dynamical processes in the CPMs provides more reliable local estimates of change, which are essential for policymakers to plan adaptation measures.

极端降水事件对社会造成巨大影响，在气候变化的影响下，情况将进一步恶化。决策者需要对未来变化进行可靠的估计，作为有效适应策略的基础，但区域气候模型（RCM）在当地尺度上的预测具有高度不确定性。在这里，我们利用 FPS Convection 项目中生成的公里级对流允许多模型 (CPM) 系综，为大阿尔卑斯地区局部降水极端事件和相关不确定性的变化提供新的认识。在对流占主导地位的夏季，CPM 系综显示出极端事件的贡献分数比驱动 RCM 系综更强。我们发现 CPM 系综大大降低了模型的不确定性及其对总不确定性的贡献超过 50%。我们的结论是，CPM 中对当地动态过程的更真实的表示提供了更可靠的当地变化估计，这对于政策制定者规划适应措施至关重要。