<p>Land surface albedo plays a key role in regulating surface energy budgets and thereby land-atmosphere interactions. Most land surface models employ a so-called soil color parameter to determine albedos for dry/saturated soils, yet it is poorly characterized in the Weather Research and Forecasting (WRF) model, leading to an underestimation of summertime surface albedo on the Tibetan Plateau (TP). This study introduces an optimized soil color map, which can effectively enhance the simulation of surface albedo and land surface temperature over a 10-year climate modeling period. This improvement reduces the TP’s thermal effect, along with decreased net radiation, latent, and sensible heat during the summer, leading to an increase in geopotential height in the lower troposphere and a reduction in water vapor flux convergence. The resulting precipitation estimation shows a reduced wet bias (52% to 36%), as compared with IMERG and GSMaP products. Further evaluation against rain-gauge observations suggests that the incorporation of an optimized soil color map improves precipitation simulation at 66% of the stations, with mean wet bias mitigated from 1.02 to 0.82 mm d^-1. The findings reveal that the underestimation of surface albedo is partially responsible for the wet bias in precipitation simulation. It also highlights the feasibility of improving climate modeling by optimizing land surface parameters, which is highly affordable through the joint use of land surface models and satellite remote sensing products.</p>