## 3 Results
### Temporal and Spatial Characteristics of Precipitation Data  
Figure 10 indicates the calibrated annual precipitation of the three satellite precipitation data and the merged annual precipitation. Figure 10 shows the calibrated and merged monthly cumulative precipitation changes in different satellite precipitation data during the study period, and the correlation analysis between MSP monthly precipitation and rain gauge data, respectively. From the above image analysis, the calibrated and merged satellite precipitation based on the three satellite data show similar spatial trends to the original satellite precipitation estimates. There is greater precipitation in the southeast and less in the northwest. Precipitation decreases from southeast to northwest, consistent with the influence of the southeast monsoon. Compared with the coarse resolution of the original satellite data, both the calibrated and merged satellite precipitation results show a greater amount of spatial detail and more obvious local spatial variation; furthermore, the merged satellite precipitation data provide more complete precipitation spatial information than single satellite precipitation data, and the precipitation distribution is more accurate (Figure 10) [27]. It is worth noting that, according to the previous article and the interannual precipitation cumulative distribution map, it can be concluded that the spatial variation of the downscaled and the merged satellite precipitation is more in line with the real precipitation distribution.  
Figure 10: _Cont_.  
From the monthly cumulative precipitation results, it could be concluded that the downscaled and calibrated precipitation has a consistent correlation with the rain gauge observations (Figure 11). The quality of the calibrated monthly cumulative satellite precipitation data has also been improved (ERA5: CC from 0.925 to 0.945, CMORPH: CC from 0.895 to 0.951, IMERG: CC from 0.967 to 0.971, MSP: CC = 0.969). Areas with a higher level of precipitation after downscaling and calibration also have greater levels of actual precipitation. Rainfall overestimation mainly occurs in periods of sufficient precipitation (summer). The merged satellite precipitation corrects the original satellite data to a certain extent, thereby reducing the difference between it and the rain gauge observation data, meaning the merged precipitation data are more accurate.  
The result of the merged satellite precipitation data is influenced by elevation. As shown in Table 8, among the nine rain gauges in the validation group, the average CC of rain gauges above 3500 m is 0.783, and the average CC of rain gauges below 3500 m is 0.80. The results demonstrate that the downscaling, calibration and merging effect of precipitation data basically presents a trend of worsening quality with the increase in altitude. On the one hand, due to the sparse and uneven distribution of rain gauges in high-altitude areas, it is hard to satisfy the needs of rain gauge calibration. On the other hand, the estimation of precipitation data by satellites in high-altitude areas is influenced by the terrain and other complex environmental and climatic factors [112].  
Figure 10: Spatial distribution map of annual precipitation data after three types of satellite downscaling and calibration in the Three-River Headwaters region in **(a)**: 2011 and **(b)**: 2013. C denotes the calibrated precipitation data and MSP means the merged satellite precipitation data.