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The Weather Research and Forecasting (WRF) model is usedin this study to simulate six storm events in two semi-humid catchments ofnorthern China. The six storm events are classified into four types based onthe rainfall evenness in the spatial and temporal dimensions. Twomicrophysics, two planetary boundary layers (PBL) and three cumulusparameterizations are combined to develop an ensemble containing 16 membersfor rainfall generation. The WRF model performs the best for type 1 events with relatively even distributions of rainfall in both space and time. Theaverage relative error (ARE) for the cumulative rainfall amount is15.82 %. For the spatial rainfall simulation, the lowest root mean squareerror (RMSE) is found with event II (0.4007), which has the most even spatialdistribution, and for the temporal simulation the lowest RMSE is found withevent I (1.0218), which has the most even temporal distribution. The most difficult to reproduce are foundto be the very convective storms with unevenspatiotemporal distributions (type 4 event), and the average relative errorfor the cumulative rainfall amounts is up to 66.37 %.The RMSEresults of event III, with the most uneven spatial and temporal distribution,are 0.9688 for the spatial simulation and 2.5327 for the temporalsimulation, which are much higher than the other storms. The generalperformance of the current WRF physical parameterizations is discussed. TheBetts–Miller–Janjic (BMJ) scheme is found to be unsuitable for rainfall simulationin the study sites. For type 1, 2 and 4 storms, member 4 performs the best.For type 3 storms, members 5 and 7 are the better choice. More guidance isprovided for choosing among the physical parameterizations for accuraterainfall simulations of different storm types in the study area

Numerical rainfall simulation with different spatial and temporal evenness by using a WRF multiphysics ensemble