Abstract: Precipitation products, including the Global Precipitation Measurement (GPM) mission, have been widely used in river basin studies due to their advantages like continuous distributions and broad spatial ranges. However, they are limited by insufficient accuracy and low spatial resolution. Based on the random forest (RF), this study integrated multisource influencing factors to generate two daily precipitation products with high spatial resolution: RF1 and RF2. The two daily precipitation products were input to the Hydrologic Engineering Center’s Hydrologic Modeling System (HEC-HMS) model to simulate daily runoff changes in the Xinjiang River basin. Finally, this study assessed the contributions of RF1 and RF2 to the improvement of GPM’s hydrologic applicability. The results show that both RF1 and RF2 improved the accuracy and distribution details of GPM data. RF2 exhibited a higher correlation and lower error, whereas RF1 manifested superior performance in detecting precipitation events. The RF1-simulated runoff curves resembled GPM-derived curves, showing significant improvements. RF2 corrected partial GPM’s overestimates and more accurately revealed the peak values of real flow curves in some periods. However, the uneven distribution of monitoring stations affected RF2’s prediction in complex terrain areas, limiting its simulation accuracy. Overall, both RF1 and RF2 can effectively reflect daily precipitation changes in the Xinjiang River basin, improving GPM’s hydrologic applicability to varying degrees.