Extreme weather events have increased significantly in the past decades due to global warming. As a robust forecast and monitoring tool of extreme weather events, regional climate models have been widely applied on local scales. This study presented a simulation of an extreme precipitation event in the Southeastern Coastal Region of China (SEC), where floods, typhoons, and mountain torrents occur frequently using the Weather Research and Forecast model (WRF) driven by GEFS (The Global Ensemble Forecast System) ensemble members (one control run and 20 ensemble members) from 01 UTC 14 June to 18 UTC 16 June 2010. The observations of hourly precipitation records from 68 meteorological stations in the SEC were applied to validate the WRF ensemble simulations with respect to 3-hourly cumulative precipitation (3hP), 6-hourly cumulative precipitation (6hP) and total cumulative precipitation (TCP). The results showed that all WRF 20 ensemble outputs could capture the extreme precipitation events fairly well with the Pearson correlation coefficient ranging from 0.01 to 0.82 and 0.16 to 0.89 for 3 and 6hP, respectively. The normalized root mean square error was comparable between the control run and 20 ensembles for 3hP (0.67 vs. 0.63) and 6hP (0.51 vs. 0.53). In general, WRF underestimated the observations for TCP. The control run (En00) modeled 28.1% less precipitation, while the 20 ensembles modeled 3.9% to 55.5% less precipitation than observations. The ensemble member 12 (En12) showed the best TCP simulation with the smallest bias. The average of 20 ensembles simulated 31.7% less precipitation than observations. The total precipitation was not captured by WRF with a significant bias that ranged from −203.1 to 112.3 mm. The storm centers were generally not captured by WRF in this case study. WRF ensembles underestimated the observation in the central Fujian Province while overestimated in the northern and southern Fujian Province. Although the average of ensembles can reduce the uncertainty to a certain extent, the individual ensemble (e.g., En12) may be more reliable on local scales
