Ensemble forecasting has emerged as a crucial method for enhancing quantitative precipitation forecasting in operational meteorology.To advance our understanding and improve effectiveness of ensemble predictions,it is imperative to rigorously and accurately assess the predictive skills of ensemble forecast systems.This study offers a comprehensive evaluation of prediction performance using statistical scoring methods such as Threat Score (TS),Equitable Threat Score (ETS),and spatial forecast verification (SAL) based on precipitation forecasts from perturbation and control ensemble members.Our results reveal the following:1) Ensemble members that initialize disturbances based on local breeding growth processes exhibit distinct advantages in forecasting heavy rainfall,yielding predictions that closely align with observed precipitation intensity and spatial distribution.2) Among all ensemble members,member e003 demonstrates the highest TS and ETS scores,along with the lowest false alarm rate and missing alarm rate.These scores are associated with superior accuracy in forecasting rainfall intensity (A) and rain area (L) in SAL verification.Conversely,member e008 displays the lowest scores for TS and ETS related to heavy rainfall,indicating a certain positional deviation in the SAL evaluation.3) Model precipitation forecast bias primarily arises from deviations in forecasting the low vortex system.Furthermore,the evaluation results of precipitation forecast scores for proficient ensemble members tend to exhibit a high degree of consistency.