Comparative evaluation of cloud microphysics schemes has always been a focal point of meteorological research. Existing cloud microphysics schemes still face uncertainties. This study focuses on a cold vortex-induced heavy rainfall event in Northeast China in July 2023, using the operational China Meteorological Administration Mesoscale Model (CMA-MESO) to conduct comparative retrospective experiments with the single-moment microphysics scheme WSM6 (namely the Weather Research and Forecasting model Single Moment 6-class scheme) and the double-moment scheme called LIUMA. The goal is to evaluate the forecasting capabilities of the schemes for cold vortex-induced heavy rainfall. The results show that both schemes could generally reproduce this heavy rainfall event; however, the LIUMA scheme’s precipitation time series exhibited a correlation coefficient of 0.75 and a root mean square error (RMSE) of 0.67 mm h⁻¹, which are closer to observations compared to WSM6’s values of 0.70 and 1.15 mm h⁻¹, respectively. It is found that WSM6 overestimated precipitation intensity more significantly. Additionally, the raindrop size distributions (RSDs) from LIUMA are more consistent with the observations. The net latent heat peak in WSM6 is 2.2 × 10⁻⁴ K s⁻¹, higher than that of LIUMA of 2.0 × 10⁻⁴ K s⁻¹. The stronger latent heating in WSM6 enhanced dynamic effects, leading to more vigorous convection and overestimated precipitation intensity. The mixing ratios of ice-phase and liquid-phase hydrometeors simulated by LIUMA are higher than those by WSM6, with overestimation of ice-phase particles being particularly pronounced. This may be one of the reasons why the LIUMA simulated radar reflectivity is significantly stronger than the observations. The main differences between the two schemes lie in the representation of ice-phase processes and the interactions between ice-phase and liquid-phase particles, and a more detailed evaluation of these processes will require advanced cloud observation techniques. The results obtained from this study help better understand and further improve the CMA-MESO operational model performance in forecasting heavy rainfall.

The horizontal resolution of global numerical weather prediction models is continuously developing. However, due to the imperfect precipitation simulation/forecast of these models, the demand for considering riming particles in cloud microphysical schemes in these models is increasing. This study employed the latest versions of global atmospheric reanalysis data (ERA5), the satellite retrieval data of the Global Precipitation Observation Program (GPM), and station precipitation observations to explore the impacts of adding graupel to the cloud microphysical scheme in the China Meteorological Administration-Global Forecast System (CMA-GFS) on summer regional precipitation simulations in four Chinese climate zones. The results verify that the new graupel scheme can enable CMA-GFS to decently predict global graupel distribution, especially in tropical and midlatitude regions. The addition of graupel in the cloud microphysics increases the precipitation simulation in North China, while that in Southwest China is weakened and dispersed. Moreover, graupel scheme increases the precipitation simulations of almost all magnitudes. The increase in light rain is obvious, and the absolute value of heavy rain is strengthened. This may be because graupel quickly melts into rain after falling out of the zero-temperature layer due to its large mass and fast falling speed, increasing surface precipitation. In summary, the addition of graupel in the cloud microphysical scheme can improve CMA-GFS’s underestimation of strong precipitation.