Risk-based event attribution (EA) science involves probabilistically estimating alterations of the likelihoods of particular weather events, such as heat waves and heavy rainfall, owing to global warming, and has been considered as an effective approach with regard to climate change adaptation. However, risk-based EA for heavy rain events remains challenging because, unlike extreme temperature events, which often have a scale of thousands of kilometres, heavy rainfall occurrences depend on mesoscale rainfall systems and regional geographies that cannot be resolved using general circulation models (GCMs) that are currently employed for risk-based EA. Herein, we use GCM large-ensemble simulations and high-resolution downscaled products with a 20-km non-hydrostatic regional climate model (RCM), whose boundary conditions are obtained from all available GCM ensemble simulations, to show that anthropogenic warming increased the risk of two record-breaking regional heavy rainfall events in 2017 and 2018 over western Japan. The events are examined from the perspective of rainfall statistics simulated by the RCM and from the perspective of background large-scale circulation fields simulated by the GCM. In the 2017 case, precipitous terrain and a static pressure pattern in the synoptic field helped reduce uncertainty in the dynamical components, whereas in the 2018 case, a static pressure pattern in the synoptic field provided favourable conditions for event occurrence through a moisture increase under warmer climate. These findings show that successful risk-based EA for regional extreme rainfall relies on the degree to which uncertainty induced by the dynamic components is reduced by background conditioning.

基于风险的事件归因（EA）科学涉及概率估计由于全球变暖导致的特定天气事件（如热浪和大雨）的可能性的变化，并且被认为是适应气候变化的有效方法。但是，针对暴雨事件的基于风险的EA仍然具有挑战性，因为与通常具有数千公里规模的极端温度事件不同，暴雨事件的发生取决于中尺度的降雨系统和无法使用通用环流模型（GCM ）（目前用于基于风险的EA）。在此，我们使用GCM大集合模拟和高分辨率缩小的产品以及20公里的非静水区域气候模型（RCM），其边界条件是从所有可用的GCM集成模拟中获得的，表明人为变暖增加了2017年和2018年日本西部发生两次破纪录的区域性强降雨事件的风险。从RCM模拟的降雨统计数据和GCM模拟的背景大尺度环流场的角度研究了这些事件。在2017年的情况下，陡峭的地形和天气区中的静压力模式有助于减少动力分量的不确定性，而在2018年的情况下，天气中的静压力模式为通过在温暖的天气中增加水分提供了有利的事件发生条件。气候。