Precipitation is a fundamental element of hydrological analyses, water resource management, and drought monitoring. To obtain high-spatiotemporal-resolution precipitation information, in this study, we proposed a merging method to integrate four satellite precipitation products (i.e., the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation 3B42 in Real-Time (TMPA-3B42RT), Climate Precipitation Center Morphing Technique (CMORPH), Global Satellite Mapping of Precipitation Near-Real-Time (GSMaP_NRT) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) products) with gauge observations. First, geographically weighted regression (GWR) was adopted to downscale the four original satellite products. Second, six base learners of stacking algorithm were used to correct the deviation in each downscaled satellite product. Third, the six corrected results of each downscaled product were integrated by a secondary learner of stacking algorithm. Fourth, the ensemble model output statistics-censored, shifted gamma (EMOS-CSG) method was adopted to produce the final precipitation product by merging the four satellite product-stacking results. The merging method was applied to the Beimiaoji basin from April to October in the 2016–2019 period. The results showed that (1) the daily merged precipitation product had a better performance than the original satellite products in terms of the six considered statistical indexes, with the lowest root mean square error (RMSE) at 4.33 mm and the highest correlation coefficient (CC) at 0.64; (2) the utilized merging method not only increased the spatial resolution to 1 km but also captured more detailed precipitation distribution information; and (3) considering the influence of the gauge density, the quality of the merged product was not further influenced after the number of gauge stations surpassed 16.