## 5 Conclusions  
In this study, a daily scale GWR precipitation downscaling model based on the correlation between cloud optical, physical properties (CER, COT, and CWP) and precipitation is proposed. Three different types of satellite precipitation data (i.e., CMORPH, IMERG, and ERA5) were used for downscaling (from 0.25\({}^{\circ}\) and 0.1\({}^{\circ}\) to 1 km). Based on the downscaled results, the GDA and GRA methods were employed to integrate rainfall data for calibration and verification. Meanwhile, the subjective and objective weighting method combining AHP and EW was adopted to merge multi-source satellite calibration data to further improve the quality of the precipitation data products. The scheme was applied to the Three-River Headwaters region for the period of 2010-2014, and the research results are as follows.  
The three kinds of original satellite precipitation data and rain gauge data have an obvious linear correlation and consistency (CC: 0.542, 0.615, and 0.644, respectively); however, there were various levels of underestimation or overestimation of the actual precipitation.  
Among the original satellite data, ERA5 and IMERG had good correlation with cloud attributes, and the data quality was significantly improved after downscaling (IMERG: CC from 0.644 to 0.691, and ERA5: CC from 0.542 to 0.565). The remaining satellite data were also improved on the basis of the original data accuracy (CMORPH: CC from 0.615 to 0.631), which indicates that it is feasible to construct a GWR model of the correlation between cloud attributes and precipitation on a daily scale. This also demonstrates the applicability of this model to various satellite data.  
The calibration and validation of downscaled precipitation with integrated rain gauge data are critical steps to help improve the quality of downscaled precipitation data. After the calibration and validation of the downscaling results of different satellite precipitation data using GDA and GRA methods, the accuracy of the downscaling results was significantly improved (IMERG: CC from 0.691 to 0.760, CMORPH: CC from 0.631 to 0.733, and ERA5: CC from 0.565 to 0.672). The IMERG data achieved the best downscaling and calibration results. For different satellite data, the GDA method obtained the optimal calibration results compared with the GRA method. In this study, the GDA_RBF method was better.  
In the fusion step, considering the data quality evaluation index, the subjective and objective weighting method combining AHP and EW was adopted to merge the multi-source satellite-calibrated precipitation data. Compared with any single-satellite calibrated precipitation datum, the merged satellite precipitation had better performance (CC = 0.790, RMSE = 2.189 mm/day, and BIAS = 0.142 mm).  
In summary, the solution proposed in this research based on the daily scale cloud attribute, precipitation GWR downscaling model and the fusion rain gauge calibration scheme is applicable in the Three-River Headwaters region, which has a complex terrain and harsh climatic conditions. At the same time, the combination of AHP and EW in the multi-source satellite data fusion technology method utilized in this paper further improved the quality of daily scale satellite precipitation data in the study area.  
Conceptualization, W.Z.; methodology, C.X. and C.L.; software, C.X. and C.L.; validation, C.X. and C.L.; data curation, Z.L. and B.A.; writing--original draft preparation, C.X. and C.L.; writing--review and editing, W.Z.; supervision, W.Z. All authors have read and agreed to the published version of the manuscript.  
This research was jointly funded by the Innovative Research Program of the International Research Center of Big Data for Sustainable Development Goals [Grant No. CBAS2022 IRP08] and the Key R&D and Transformation Program of Qinghai Province [Grant No. 2020-SF-C37].  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.