Based on the daily 24-168h ensemble precipitation forecast datasets, which are derived from the European Centre for Medium-Range Weather Forecasts extracted from the TIGGE (The Interactive Grand Global Ensemble) dataset. The ensemble forecast consists of 51 ensemble members. Using the left-censored non-homogeneous logistic regression method (CNLR) and standardized model post-processing method (SAMOS) to calibrate the precipitation forecasts in the southeast of China. The results show that the CNLR method can effectively improve the mean absolute error (MAE) and continuous ranked probability score (CRPS) of the raw ensemble forecast, and improve the forecasting skills of quantitative and probabilistic precipitation forecasts. Using SAMOS method to preprocess the data and considering the impact of topography and other factors, the forecast error caused by the terrain influence can be further revised on the basis of CNLR method, and a more accurate probabilistic forecast of precipitation is thus obtained.