###### Abstract
In response to this problem, the proposal of the local regression model of geographically weighted regression (GWR) is particularly important for downscaling of satellite precipitation data [59]. Geographically weighted regression models have been introduced to downscale satellite data in many regions of China. Compared with other downscaling methods, the NDVI, DEM and TRMM methods provide data that fit better and obtain more accurate precipitation data [60; 61]. The GWR model precipitation downscaling method mainly relies on the correlation between precipitation and other environmental factors, which improves the spatial resolution of satellite precipitation data to a certain extent. However, the robustness of GWR model is affected by the different geographical conditions and the accuracy of the original satellite data, which limits the improvement of its downscaling accuracy [43; 62]. Therefore, it is necessary to combine satellite precipitation data with rain gauge observations to improve the quality of precipitation data products [63; 64]. Commonly proposed statistical merging schemes primarily include statistical objective analysis [65], kriging with external drift [64], Kalman filter [66], minimum error estimation [67], optimal interpolation method [68], geographic ratio analysis (GRA) [31] and geographic difference analysis (GDA) [4]. Based on the relationship between precipitation and other environmental variables, these downscaling methods that fuse the GWR precipitation downscaling model with rain gauge data have achieved satisfactory results; however, they also have some deficiencies. The above-mentioned downscaling methods are mainly conducted at the climatic scale, such as monthly and annually [20; 31], ignoring the influence of extreme precipitation events, which are still not suitable for use in climate applications such as hydrological modeling and flood prediction. Due to the short-term invariant nature of auxiliary variables such as DEM and slope, and the hysteresis effect of NDVI on precipitation events [69], it is hard to directly show the impact of the above surface environmental variables on daily precipitation events. Research has shown that there is a strong response relationship between cloud optical thickness (COT) and precipitation rate and precipitation amount [70; 71]. For example, based on the optical and physical properties of clouds and precipitation in northeastern Austria, multiple linear regression and artificial neural network methods were applied to downscale the satellite precipitation data for 5 days in 2015, and good results were obtained (correlation coefficients from 0.30 to 0.56) [72]. The Geographic Moving Window Weight Decomposition Analysis (GMWWDA) method was also proposed in another study to explore the relationship between precipitation and four cloud characteristics in the southeast coast of China. The hourly precipitation data for 17 August 2018, were downscaled and the results show that cloud effective radius (CER) downscaling works best (correlation coefficients from 0.53 to 0.57) [73]. Therefore, it should be feasible to select cloud attributes as auxiliary environmental variables for precipitation downscaling. Meanwhile, the fusion of multi-source precipitation data was also employed as a method to further improve satellite precipitation data [74], and improved analysis results were achieved [30; 44]. A variety of fusion methods have been proposed, including weighted fusion [75], optimal interpolation [64], a framework based on triple collocation [76] and a random-forest-based merging procedure [77]. The determination of weight in data fusion is a critical issue, and the selection of the weight index directly influences the result of data fusion [78]. The subjective and objective combination weighting method shows great potential in solving the problem of weight determination [79].