This study used the Weather Research and Forecasting Model WRF V4.0.2 (Weather Research and Forecasting Model, Version 4.0.2) to simulate two Meiyu front precipitation processes in Zhejiang Province, and to investigate the influence of different cumulus convective parameterization schemes on precipitation forecast. The WSM6 and Thompson microphysics schemes, YSU and MYJ boundary layer schemes, and 11 cumulus convective parameterization schemes were selected for comparative analyses to explore the influence of different cumulus convective parameterization schemes on Meiyu front precipitation forecast. The results show that: (1) In the process of precipitation forecast evaluation for each experiment, both the traditional point-to-point method and the neighborhood method can objectively show the prediction level of each experiment. However, the neighborhood method performs more objectively evaluation of the prediction level of small-scale heavy precipitation. (2) Three types of cumulus convection solutions (no cumulus clouds, traditional cumulus convection, and the scale-aware cumulus convection) can better simulate the light precipitation, but with the intensification of precipitation to ranstorm and heavy rainstorm, the scale-aware cumulus convection schemes improves the forecast results significantly. (3) The scale-aware cumulus convection scheme is more sensitive to microphysics and planetary boundary layer schemes, and the simulation results of the scale-aware cumulus convection scheme are more significant under different microphysics and boundary layer combination schemes, while the simulation results of the traditional cumulus convection scheme are not obvious. (4) In the "gray zone" range of 1-10 km, the scale-aware cumulus convection scheme can significantly improve the prediction results of model compared with the traditional cumulus convection scheme when the grid resolution is increased to 1 km. To some extent, the results of this study can provide a reference for the application of scale-aware convective parameterization schemes in high-precision operational forecasting.