## 3 Results
### The Relationship between Cloud Physical and Optical Properties and Precipitation  
Before downscaling precipitation through the correlation between cloud attributes and satellite precipitation data, the correlations between selected auxiliary variables and three types of satellite data were analyzed, as shown in Table 3. The results shows that the CWP has the best correlation with the satellite precipitation data, and the lowest correlation was found for the CER. Among the satellite precipitation data, ERA5 and IMERG show a fine correlation with cloud attributes, and the CMORPH shows the weakest. It can be inferred that the GWR downscaling model constructed based on cloud attributes and satellite precipitation data should be better applied to ERA5 and IMERG. The validation of the precipitation downscaling results of the GWR model below also proves this. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **CC** & **RMSE (mm)** & **BIAS (mm)** \\ \hline IMERG & 0.644 & 3.091 & \(-\)0.013 \\ CMORPH & 0.615 & 3.081 & 0.050 \\ ERA5 & 0.542 & 3.852 & 0.885 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Original quality of three satellite precipitation data in the period of 2010–2014. Figure 3: Scatter plots showing relationship between daily rain gauge observations and (**a**) the original CMORPH, (**b**) the original ERA5, (**c**) the original IMERG at one selected station location in the period of 2010–2014. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **CER** & **COT** & **CWP** & **Average** \\ \hline CMORPH & 0.120 & 0.212 & 0.229 & 0.187 \\ ERA5 & 0.169 & 0.270 & 0.252 & 0.230 \\ IMERG & 0.144 & 0.240 & 0.259 & 0.214 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Correlation between three kinds of original satellite precipitation data and cloud attribute data. Evaluation of Downscaling Accuracy of Daily Scale Satellite Precipitation Datasets Based on GWR Model  
The downscaled results of daily precipitation for three different satellites are shown in Figure 4, and the accuracy validation results with 29 rain gauge observations are shown in Table 4. Compared with original satellite data, the GWR model combines satellite-based precipitation observations with corresponding cloud properties, and the downscaled results improve the accuracy of precipitation data while reducing RMSE and BIAS (Table 4). Downscaled satellite precipitation based on cloud attributes describes spatial precipitation distribution in more detail at a 1 km resolution (Figure 5). After downscaling, the best correlation coefficients for CC are IMERG (CC: 0.691), the ones with the lowest RMSE and BIAS are also obtained for IMERG (IMERG_RMSE: 2.623 mm/day, IMERG_BIAS: 0.002 mm/day, respectively). The improvement of the three satellite precipitation data after downscaling are as follows: IMERG--CC from 0.644 to 0.691; CMORPH--CC from 0.615 to 0.631; and ERA5--CC from 0.542 to 0.565 (Table 4). These results also prove that our previous point of inferring the effect of downscaling based on the correlation between cloud attributes and different satellite precipitation data is correct. By analyzing the precision of the downscaled precipitation, it was found that the downscaled precipitation data obtained better data quality and smaller errors compared with the original satellite precipitation data (Figure 6). The quality of daily precipitation data after downscaling through the GWR model has improved; however, there is still an overestimation of the actual precipitation levels. Since the quality of downscaled predictions is restricted by the inherent precision of the original satellite precipitation product, it is vital to combine the downscaled precipitation data with the RGS data to improve the quality of the precipitation data. Figure 4: Boxplots of statistical metrics of downscaled daily IMERG, CMORPH, and ERA5 precipitation data from 29 rain gauge stations: (**a**) CC, (**b**) RMSE, and (**c**) BIAS.