<p>Climate models widely simulate excessive precipitation over the Tibetan Plateau (TP) (i.e., the model wet bias), particularly in the summer season. This significantly hinders our understanding of regional climate changes and their impacts on the East Asian monsoon circulation and the global climate system. This study proposes an approach of adjusting the critical relative humidity (RH_c) in the cloud microphysical parameterization to alleviate the precipitation bias within the Weather Research and Forecast (WRF) model. By implementing a vertically varying RH_c profile, this approach enhances warm cloud processes while suppressing ice-phase cloud processes, leading to increased liquid cloud content throughout the day and reduced ice-phase precipitation particles before local noon. This renders the MP precipitation (calculated by the microphysics scheme) decreased (increased) before (after) the local noon, respectively. At the same time, the increased liquid cloud content reflects more incoming solar radiation and cools the surface. This reduces the convective available potential energy, suppresses the overly active convection, and reduces the CP precipitation (calculated by the convection scheme). These two pathways work together, which effectively modulates the partitioning between resolvable-scale and convective cloud processes, alters MP precipitation and CP precipitation accordingly, and ultimately alleviates the model wet bias markedly. Further studies are encouraged to refine the RH_c approach for improving its physical basis and broadening its applicability across different climate regimes.</p>