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Over‐estimation of summer precipitation over the Tibetan Plateau (TP) is a well‐known and persistent problem in most climate models. This study demonstrates the impact of a Gaussian Probability Density Function cloud fraction scheme on rainfall simulations using the Weather Research and Forecasting model. It is found that this scheme in both 0.1° and 0.05° resolutions significantly reduces the wet bias through both local feedbacks and large‐scale dynamic process. Specifically, increased cloud water/ice content with this scheme reduces surface shortwave radiation, and consequently surface heat fluxes and evapotranspiration. This, in turn, dampens the large‐scale thermal effect of the TP and weakens the exaggerated monsoon circulation and low‐level moisture convergence. It is this large‐scale dynamic process that contributes the most (∼70%) to the wet bias reduction. Although this paper presents a modeling study, it highlights the cloud radiative feedback to the large‐scale dynamics and precipitation over the TP. Plain Language Summary: Despite numerous attempts to correct the overestimation of summer precipitation over the Tibetan Plateau (TP) in current global and regional climate models, the issue persists. This study applies the Gaussian Probability Density Function (GPDF) cloud fraction scheme in the Weather Research and Forecasting model at two different resolutions (0.1° and 0.05°) during a summer over the TP. The results show that the GPDF scheme significantly mitigates the precipitation overestimation, particularly in the high‐resolution modeling. We explored the physical processes, both local and remote, that contribute to this improvement. Specifically, an increase in cloudiness reduces the amount of radiation reaching the land surface. This decrease in surface radiative heating not only reduces local evaporation but also weakens the thermal effect of the TP. The latter is a major driver of the South Asian monsoon that conveys moisture to the TP, and its weakening reduces moisture convergence over the TP. Both the decreases in local evaporation and remote moisture convergence contribute to the alleviation of the precipitation overestimation, and the latter plays a dominant role. These findings provide a unique perspective for reducing the wet bias over the TP, focusing on the surface available energy and associated remote moisture processes. Key Points: The use of the Gaussian Probability Density Function cloud fraction scheme in high resolution greatly reduces wet bias over the Tibetan Plateau (TP) during summerMore cloud water/ice with the scheme lessens TP's thermal effect, causing a weaker South Asian monsoon and moisture convergenceWet bias reduction is mainly governed by the decrease in remote moisture rather than local evapotranspiration [ABSTRACT FROM AUTHOR]