Selecting proper parameterization scheme combinations for a particular application is of great interest to the Weather Research and Forecasting (WRF) model users. This study aims to develop an objective method for identify-ing a set of scheme combinations to form a multi-physics ensemble suitable for short-range precipitation forecasting in the Greater Beijing area. The ensemble is created by using statistical techniques and some heuristics. An initial sample of 90 scheme combinations was first generated by using Latin hypercube sampling (LHS). Then, after seve-ral rounds of screening, a final ensemble of 40 combinations were chosen. The ensemble forecasts generated for both the training and verification cases using these combinations were evaluated based on several verification metrics, in-cluding threat score (TS), Brier score (BS), relative operating characteristics (ROC), and ranked probability score (RPS). The results show that TS of the final ensemble improved by 9％-33％ over that of the initial ensemble. The re-liability was improved for rain ≤ 10 mm day-1, but decreased slightly for rain > 10 mm day-1 due to insufficient samples. The resolution remained about the same. The final ensemble forecasts were better than that generated from randomly sampled scheme combinations. These results suggest that the proposed approach is an effective way to se-lect a multi-physics ensemble for generating accurate and reliable forecasts.