## 3 Results
### Data Fusion of Downscaled Precipitation with Rain Gauge Observations  
Due to the difference between satellite precipitation data and RGS during downscaling, there may be a deviation between the downscaled data based on GWR and the actual precipitation. At the same time, owing to the difficulty of accessibility and the influence of other limiting factors, the observation of the data of rain gauges in high-altitude areas usually suffers from insufficient data acquisition. Therefore, the precipitation inferred from the observation data of rain gauges also has great uncertainty [105]. Therefore, the RGS data should be fused with the downscaled satellite precipitation data to obtain more accurate precipitation data. In this study, we employed two calibration methods based on RGS data, namely GDA and GRA. RGS data are divided into the following two groups: the calibration group (twenty stations) and validation group (nine stations). Table 5 describes the validation statistics for three different satellite precipitation data calibrated with RGS over the entire time period and the original downscaled results before calibration. As shown in the analysis of the above results, both the GDA and GRA calibration methods could further improve the accuracy by increasing the CC and decreasing the RMSE and BIAS. There are differences in the calibration effects of GRA and GDA between various data sources. For the three kinds of satellite precipitation data, the calibration result of GDA is obviously better than that of GRA in the process of fusion RGS calibration. Figure 7 shows the comparison between the accuracy of the three satellite precipitation data after downscaling and the calibration of rain gauges and the accuracy of the original data. It can be concluded that the data quality after downscaling and rain gauge calibration has been significantly improved. Similarly, the results of different interpolation methods (i.e., IDW, RBF, and OK) for GDA calibration are also quite different. Table 6 reveals the statistical results obtained by different interpolation methods when GDA calibrates the three satellite downscaled precipitation data. It can be found that among the three interpolation methods, GDA_RBF achieves better results in the calibration of CMORPH, IMERG and ERA5 downscaled data, and GDA_IDW performs well in the calibration of CMORPH downscaled data. The calibration result of GDA_OK is more common. In general, the ERA5 data have the lowest original accuracy, but the data quality improvement after downscaling and rain gauge calibration is the highest. While maintaining the highest raw accuracy, the IMERG data displays a good response to GWR downscaling and rain gauge calibration operations and achieves optimal data results. CMORPH data have high raw data precision and fine downscaling and calibration results, but they are not sensitive to the GWR downscaling method based on cloud attributes. GDA_RBF performs best in the calibration verification process of downscaled satellite data based on RGS. \begin{table}
\begin{tabular}{c c c c} \hline  & **CC** & **RMSE (mm)** & **BIAS (mm)** \\ \hline IMERG\_D & 0.691 & 2.623 & 0.002 \\ IMERG\_GDA & 0.760 & 2.453 & 0.147 \\ IMERG\_GRA & 0.719 & 2.562 & 0.075 \\ CMORPH\_D & 0.631 & 2.817 & 0.076 \\ CMORPH\_GDA & 0.733 & 2.544 & 0.211 \\ CMORPH\_GRA & 0.669 & 3.134 & 0.257 \\ ERA5\_D & 0.565 & 3.620 & 1.008 \\ ERA5\_GDA & 0.672 & 2.656 & 0.103 \\ ERA5\_GRA & 0.663 & 2.657 & 0.065 \\ \hline \end{tabular} Note: D represents the downscaled precipitation data, GDA denotes precipitation data using the GDA calibration method and GRA denotes precipitation data using the GRA calibration method.  
\end{table} 
Table 5: The statistical results of un-calibrated and calibrated satellite precipitation data with rain gauge station data.