Numerical precipitation forecasts exhibit variability under differing atmospheric conditions. Graupel particles, which act as a crucial sink for rainwater, have a substantial impact on surface precipitation predictions in numerical models. This study utilizes the WRF model coupled with the Thompson dual-moment scheme to investigate the influence of various graupel particle parameters on precipitation forecasts under both strong and weak synoptic forcing. The results indicate that modifying the graupel particle density and fall velocity coefficient enhances precipitation forecasts in both scenarios. Under weak forcing, deep convection primarily governs precipitation, leading to an increase in rainwater content across various hydrometeor species. In contrast, under strong forcing, both deep convection and stratiform clouds contribute comparably to precipitation, with a marked increase in snow particle concentrations. High graupel particle density and low fall velocity coefficient predict extreme mass ratios of rainwater to graupel in both conditions. In weak forcing, the warm cloud process is more dominant, with changes in graupel particle properties affecting both vertical velocity and spatial distribution. Conversely, under strong forcing, the cold cloud process prevails, with alterations in graupel parameters primarily influencing the intensity of vertical velocity, while having minimal effects on spatial distribution.