Abstract: In order to provide more powerful support for forecasters in Sichuan and Chongqing with complicated terrain to carry out short-term (0-12 h) precipitation forecast, a convective scale ensemble forecast operational system is designed based on ensemble Kalman filter data assimilation method (31 ensemble samples) and WRF model with 3 km resolution (model domain: 24.5°-34.5°N, 99°-113°E) and lead time of 12 h, which is started by 3 h cycle. It is urgent to decide how many members should be used for the 12 h ensemble forecast to achieve the most representative probability distribution and optimal ensemble forecast skills. An ensemble forecast experiment is carried out on 16 heavy convective scale precipitation cases occurred in Sichuan and Chongqing with different amount of ensemble members, and the results are analyzed comprehensively. It is concluded that the precipitation forecast skills of the ensemble members for different magnitude of precipitation are roughly the same, so there is little difference in the totally averaged prediction skills of different ensemble size. Talagrand distribution becomes better with the increase of ensemble size first. However, when the ensemble size is larger than 17, the improvement by increasing ensemble size is no longer significant. Meanwhile, the forecast error probability becomes smaller with the increase of ensemble size first, but when the ensemble size reaches 16 to 18, the difference between the forecast error probability and the ideal value tends to be stable, indicating that the improvement by further increasing the ensemble size is no longer significant. The relative area of operational characteristic (AROC) score which represents the prediction probability forecast skills, improves gradually with the increase of ensemble size. However, when the ensemble size is large enough, the improvement by lager ensemble size is no longer significant and the AROC scores tend to be stable. The ensemble size required for stable AROC score increases with the magnitude of precipitation. Overall, when the AROC scores become stable, the ensemble size required for light rain, moderate rain, heavy rain, rainstorm (and heavy rainstorm) are 10, 14, 16 and 18, respectively. Based on the comparative analysis results and considering that there is generally little difference in forecasting skills when the number of members is different by 2, in order to achieve the most representative probability distribution and optimal ensemble forecast skills of precipitation, it is recommended to set the ensemble size of convective scale ensemble prediction system from 16 to 18.