We integrate various reanalysis, meteorological observation, and satellite precipitation datasets, including CRU (Climate Research Unit), ERA5 (European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis version5), MERRA2 (Modern-Eta Retrospective Analysis for Research and Applications version2), CMAP (Climate Prediction Center (CPC) integrated analysis of monthly precipitation), GPCP (Global Precipitation Climatology Project) and GPM (Global Precipitation Measurement), to create a new monthly precipitation product with a spatial resolution of 0.1° × 0.1° (latitude × longitude) during the period from 1980 to 2019 in arid and semi-arid regions of China (ASRC). The new product was validated with observations at different temporal scales (e.g., monthly to yearly variability). The results show that the new product improves the accuracy of precipitation with the lower bias of 1.0 mm/month and the highest correlation coefficient of 0.998 was achieved with observation. The probability distribution functions (PDF) and standardized precipitation index (SPI) of the integrated precipitation are closer to observation than other reanalysis datasets.