## 4 Discussion  
Feasibility of a GWR Downscaling Model Based on the Correlation between Cloud Attributes and Daily Precipitation Data  
The relationship between precipitation and the environment is spatially heterogeneous and scale dependent, especially in mountainous areas with a dramatic topography [33]. Therefore, considering the relationship between precipitation and the spatial variation of environmental variables, it may be more appropriate to establish a local regression downscaling model than a global regression downscaling model. The GWR model describes the relationship between precipitation and auxiliary environmental variables by means of local regression, and has been widely used in satellite precipitation data downscaling, Auxiliary environmental variables such as elevation, terrain, slope, latitude and longitude, and NDVI are adopted to construct the GWR precipitation downscaling model to downscale the annual and monthly satellite precipitation data [4; 49; 56; 60; 112].  
However, few studies have directly constructed GWR models through daily scale environmental variables and precipitation to downscale daily scale precipitation data. Relevant studies have shown that the correlation between cloud attribute data and precipitation is useful in achieving precipitation downscaling [71; 72]. Therefore, this study selects the daily cloud attributes, and realizes daily scale precipitation downscaling according to the local regression relationship between them and precipitation, which is of significant importance. The traditional method for obtaining daily and monthly precipitation is to employ the daily fraction and monthly fraction methods [30]. This method mainly relies on the accuracy of the original satellite precipitation data, and there are uncertainties. This paper introduces cloud attribute variables based on the accuracy of the original satellite  
Figure 11: (**a**) Comparison of monthly cumulative precipitation data, calibrated IMERG data, calibrated ERA5 data, calibrated CMORPH data and merged satellite precipitation data at 52,955 rain gauge station; (**b**) scatter plots between cumulative monthly observations of meteorological stations and merged satellite precipitation data monthly cumulative observations in 2010–2014.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Station ID** & **Elevation (m)** & **CC** & **RMSE (mm)** & **BIAS (mm)** \\ \hline
52856 & 2835 & 0.800 & 1.763 & 0.252 \\
52868 & 2237 & 0.789 & 1.864 & 0.560 \\
52955 & 3200 & 0.808 & 2.152 & \(-\)0.051 \\
56018 &  
data, which improves the reliability of daily scale precipitation downscaling to a certain extent. At the same time, after introducing cloud attributes as auxiliary environmental variables in the GWR model, the quality of the three satellite downscale precipitation data improved, which also proves the universality and feasibility of this research scheme.  
By constructing the GWR model of cloud attributes and precipitation data, we obtain downscaled satellite precipitation data as the basic data, which also reduces the impact of the original satellite data errors in the GDA and GRA methods, as shown in Figure 11. Simultaneously, the GDA and GRA methods that integrate the rain gauge observation data further improve the quality of the downscaled precipitation data.