Climate models tend to underestimate rainfall intensity while producing more frequent light events, leading to significant bias in extreme precipitation simulation. To reduce this bias and better understand its underlying causes, we tested an ensemble of 25 physics configurations in the regional Climate-Weather Research and Forecasting model (CWRF). All configurations were driven by the ECMWF-Interim reanalysis and continuously integrated during 1980–2015 over the contiguous United States with 30-km grid spacing. Together they represent CWRF’s ability to simulate characteristics of US extreme precipitation, and their spread depicts the structural uncertainty from alternate physics parameterizations. The US extreme precipitation simulation was most sensitive to cumulus parameterization among all physics configurations. The ensemble cumulus parameterization (ECP) was overall the most skilled at reproducing seasonal mean spatial patterns of daily 95th percentile precipitation (P95). Other cumulus schemes severely underestimated P95, especially over the Gulf States and the Central-Midwest States in convective prevailing seasons. CWRF with ECP outperformed the driving reanalysis, which substantially underestimated P95 despite its daily atmospheric moisture data assimilation. The CWRF improvement over ERI is much larger in warm than cold seasons. Changing alone ECP closure assumptions produced two distinct clusters of convective heating/drying effects: one altered P95 mainly by changing total precipitation intensity and another by changing rainy-day frequency. Microphysics, radiation, boundary layer, and land surface processes also impacted the result, especially under mixed synoptic and convective forcings, and some of their parameterization schemes worked with ECP to further improve P95.

气候模型往往会低估降雨强度，同时产生更频繁的光照事件，从而导致极端降水模拟中的明显偏差。为了减少这种偏见并更好地了解其根本原因，我们在区域气候-天气研究和预报模型（CWRF）中测试了25种物理配置的集合。所有配置均由ECMWF-Interim重新分析驱动，并在1980-2015年期间以30公里的网格间距连续整合在美国各地。它们一起代表了CWRF模拟美国极端降水特征的能力，它们的扩散描述了替代物理参数化带来的结构不确定性。在所有物理学配置中，美国极端降水模拟对积云参数化最为敏感。总的来说，集成体积参数化（ECP）最​​擅长再现每日第95个百分位数降水（P95）的季节性平均空间格局。其他积水计划严重低估了P95，特别是在对流盛行季节的海湾国家和中西部地区。带有ECP的CWRF优于驾驶重新分析，尽管P95每天都会吸收大气中的水分，但仍大大低估了P95。与暖季相比，暖季的CWRF比ERI的改善要大得多。单独改变ECP封闭假设会产生两个不同的对流加热/干燥效应集群：一个主要通过改变总降水强度来改变P95，另一个通过改变雨天频率来改变P95。微观物理学，辐射，边界层和陆地表面过程也影响了结果，