Satellite quantitative precipitation estimation (QPE) can make up for the insufficiency of ground observations for monitoring precipitation. Using an Advanced Geosynchronous Radiation Imager (AGRI) on the FengYun-4A (FY-4A) satellite and rain gauges (RGs) for observations in the summer of 2020. The existing QPE of the FY-4A was evaluated and found to present poor accuracy over the complex topography of Western China. Therefore, to improve the existing QPE, first, cloud classification thresholds for the FY-4A were established with the dynamic clustering method to identify convective clouds. These thresholds consist of the brightness temperatures (TBs) of FY-4A water vapor and infrared channels, and their TB difference. Then, quantitative cloud growth rate correction factors were introduced to improve the QPE of the convective-stratiform technique. This was achieved using TB hourly variation rates of long-wave infrared channel 12, which is able to characterize the evolution of clouds. Finally, the dynamic time integration method was designed to solve the inconsistent time matching between the FY-4A and RGs. Consequently, the QPE accuracy of the FY-4A was improved. Compared with the existing QPE of the FY-4A, the correlation coefficient between the improved QPE of the FY-4A and the RG hourly precipitation increased from 0.208 to 0.492, with the mean relative error and root mean squared error decreasing from −47.4% and 13.78 mm to 8.3% and 10.04 mm, respectively. However, the correlation coefficient is not sufficiently high; thus, the algorithm needs to be further studied and improved.

Exploring the characteristics of uneven temporal and spatial distribution of precipitation in mountain watersheds can provide a reference for regional agricultural development and resource utilization, and contribute to the protection of the ecological environment. Based on the daily precipitation observation data of 40 meteorological stations in the Wujiang River Basin from 1963 to 2021, the temporal- and spatial-variation characteristics of the precipitation concentration degree (PCD) and precipitation concentration period (PCP) were analyzed using the Randall S analysis method, Mann–Kendall test method, Pettitt method, wavelet analysis and empirical orthogonal function (EOF). The results showed that the fluctuation range of PCD in Wujiang River Basin from 1963 to 2021 was 0.34–0.59, with a multi-year average of 0.47, which was obviously higher than the national average level and is showing a trend of slowly increasing. The fluctuation range of PCP was between 17.1 and 21.5 days, with a multi-year average of 19.0. The annual precipitation was mostly concentrated around the middle of July and showed a slowly decreasing trend. The abrupt change in PCD and PCP occurred around 1983 and 2001, respectively. There is an obvious Hearst phenomenon in PCP. In the future, the trend of precipitation concentration in the middle period will remain in advance, and the degree of precipitation concentration will continue to increase. The maximum precipitation in the flood season will continue to be delayed. The spatial pattern of the first mode of PCD and PCP in the Wujiang River basin was consistent and showed an opposite pattern between the upper reaches and the middle-lower reaches of the basin, which reflects the influence of the topography of the basin on the spatial distribution of precipitation. The distribution of precipitation is affected by topography. The elevation change in the basin was complex, and the leeward slope varied a lot. Therefore, it has a significant impact on precipitation. Areas with less precipitation are at higher elevations and on mountain leeward slopes, with a lack of moist air flow. The area with more precipitation was the summer monsoon mountain windward slope, and the topography blocks the increase in precipitation. The elevation of the central region is relatively uniform and the terrain is flat. Therefore, the distribution of precipitation is more uniform.

The Fourier–Merlin transform method, multi-scale optical flow method, and Weibull distribution are used to integrate the GRAPES_3 km model and Radar Extrapolation Forecast (REF) both developed independently by China. Taking GRAPES_3 km, Wuhan Rapid Update Cycle (WHRUC), and the REF as examples, the prediction performance of the Blending forecast is evaluated comprehensively by the traditional point-to-point method. A new spatial test method is introduced to evaluate the applicability and difference of high-resolution model evaluation. The area, position, shape, and intensity of the precipitation area are matched through the target object test method. The potential forecast information of the spatial field is obtained and the related results are compared and analyzed. The results show that: (1) the comprehensive application of various evaluation methods can evaluate the convective storm forecast more comprehensively. The Blending forecast effect is obviously better than those of other models by using the point-to-point scoring method, especially in the heavy precipitation forecast. The shorter the prediction time is, the better the effect is. (2) The new spatial test method can evaluate the prediction effect of convective storm characteristics, and the target recognition hit rate of the Blending forecast is highest. The scores of target area, position, shape, and median intensity of precipitation are better than those of other forecasts. The variation in the east–west direction is less than that in the north–south direction, which is basically consistent with the actual observation. The variation range of the forecast grid before and after translation is the closest to the reality. (3) The Blending forecast method combines the advantages and disadvantages of the numerical model and REF, which can not only grasp the precipitation area but also improve the prediction ability of rainfall intensity. The traditional point-to-point scoring method and the new spatial test method have the same conclusion as the convective storm forecast of the high-resolution model, which has a certain reference value, and the new spatial test method can provide more detailed evaluation information.