Global warming leads to more frequent and more violent extreme weather events, which cause severe natural disasters. The sensitivity of physical schemes in numerical weather prediction models for extreme precipitation is a significant challenge. To simulate a heavy precipitation process from 1 July 2020 to 10 July 2020 over the Poyang Lake Basin, where floods occur frequently, the Weather Research and Forecast model (WRF) was employed. The observation (OBS) from 92 meteorological stations was applied to evaluate WRF performance. To assess the optimal parameter, 27 combinations of multiphysics schemes based on three cumulus schemes (CUs), three microphysical schemes (MPs) and three land surface schemes (LSMs) were employed in WRF simulation. The Euclid distance (ED) was derived to evaluate the performance of the modelled total cumulative precipitation (TCP). The results showed that the simulation generally reproduced the spatial distribution of precipitation and captured the storm centre. In general, WRF underestimated the observation for most areas but overestimated the observation in the northeastern part of the basin. For total cumulative precipitation (TCP), the spatial correlation coefficients ranged from 0.6 to 0.8. M11 had the highest value of 0.796. The scores (TS, POD, FAR) of M15 were 0.66, 0.79, and 0.21, respectively. With the maximum similarity of 0.518, M7 (BMJ-WDM6-Noah) showed the best performance in ED based on six evaluation metrics. The mean values of OBS and M19 were 259.34 and 218.33 mm, respectively. M19 (NT-Thompson-Noah) was closest to the OBS for the range of the TCP. In terms of daily precipitation, the CC of M3 was maximum (0.96), and the RMSE of M24 was minimum (11.9 mm. day-1). The minimum error between the simulation and OBS was found for M3 (NT-Thompson-Noah). Therefore, with a comprehensive evaluation, five optimal combinations of physical schemes (M22, M19, M9, M3, M21) were found for the PLB, which is of great significance for extreme precipitation simulation and flood forecasting.