###### Abstract  
Complex terrain, the sparse distribution of rain gauges, and the poor resolution and quality of satellite data in remote areas severely restrict the development of watershed hydrological modeling, meteorology, and ecological research. In this study, based on the relationship between cloud optical and physical properties and precipitation, a daily geographically weighted regression (GWR) precipitation downscaling model was constructed for the Three Rivers Source region, China, for the period from 2010 to 2014. The GWR precipitation downscaling model combined three different satellite precipitation datasets (CMORPH, IMERG, and ERA5) which were downscaled from a coarse resolution (0.25\({}^{\circ}\) and 0.1\({}^{\circ}\)) to a fine resolution (1 km). At the same time, the preliminary downscaling results were calibrated and verified by employing the geographic difference analysis (GDA) and geographic analysis (GRA) methods combined with rainfall data. Finally, the analytical hierarchy process (AHP) and the entropy weight method (EW) were adopted to fuse the three downscaled and calibrated satellite precipitation datasets into the merged satellite precipitation dataset (MSP), which provides a higher quality of data (CC = 0.790, RMSE = 2.189 mm/day, and BIAS = 0.142 mm). In summary, the downscaling calibration and precipitation fusion scheme proposed in this study is suitable for obtaining high-resolution daily precipitation data in the Three Rivers Source region with a complex climate and topography. daily precipitation; cloud properties; geographically weighted regression; downscaling; data fusion; Three River Headwaters region +
Footnote †: journal:  
0000-0001-  
Weather Forecasts (ECMWF) [14], Global Satellite Mapping of Precipitation (GSMaP) [15], the Global Precipitation Climatology Project (GPCP) [16; 17], the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN) [18], the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) [19] and Global Precipitation Measurement (GPM) [20]. Compared with rain gauge observations, remote sensing-based satellite technologies could provide large-scale, continuous, high-frequency observations of precipitation data [21]. Relying on the strengths and availability of satellite precipitation datasets, satellite precipitation data have been widely used in applications such as drought monitoring, flood prediction, modelling of hydrological processes and the estimation of extreme precipitation events [22; 23; 24; 25; 26; 27; 28]. The application of different satellite data products in different regions has been shown to have different effects; however, two common shortcomings of these satellite precipitation datasets greatly limit their application scope [29; 30]. Firstly, satellite precipitation datasets are not able to reflect the fine-scale changes in precipitation distribution due to their coarse resolution, which limits their hydrological or meteorological applications at some regional scales [31; 32; 33]. Among the above satellite precipitation datasets, the PERSIANN-CCS dataset has the highest spatial resolution of 0.04\({}^{\circ}\)\(\times\) 0.04\({}^{\circ}\)[34]. However, this still could not meet the application needs of regional hydrology and meteorology. For example, a catastrophic hurricane in Europe failed to be reproduced by any remotely sensed precipitation dataset, according to a new study [35]. Secondly, due to the influence of regional climate types, data sources, inversion algorithms, timescales, and topographic factors, satellites underestimate or overestimate actual precipitation data to varying degrees compared with rain gauge observations [36; 37; 38]. For example, due to its own mechanism, TRMM precipitation was systematically overestimated for Asian inland water bodies [39]. CMORPH performed best in delineating the spatial pattern and temporal variation of precipitation in China [40]. CHIRPS data is the best choice when rainfall is less than 1 mm/day in dry areas of Egypt [41].