It is reported that the China–Pakistan Economic Corridor has been affected by extreme precipitation events. Since the 20th century, extreme weather events have occurred frequently, and the damage and loss caused by them have increased. In particular, the flood disaster caused by excessive extreme precipitation seriously hindered the development of the human society. Based on CRiteria Importance Through Intercriteria Correlation and square root of generalized cross-validation, this study used intensity–area–duration to analyze the trend of future extreme precipitation events, corrected the equidistance cumulative distribution function method deviation of different future scenario models (CESM2, CNRM-CM6-1, IPSL-CM6A-LR, and MIROC6) and evaluated the simulation ability of the revised model. The results showed that: 1) the deviation correction results of CNRM-CM6-1 in the Coupled Model Intercomparison Project Phase (CMIP) 6 could better simulate the precipitation data in the study area, and its single result could achieve the fitting effect of the CMIP5 multimodel ensemble average; 2) under CNRM-CM6-1, the frequency of extreme precipitation events under the three climate scenarios (SSP1-2.6, SSP3-7.0, and SSP5-8.5) presents interdecadal fluctuations of 3.215 times/10A, 1.215 times/10A, and 5.063 times/10A, respectively. The average impact area of extreme precipitation events would decrease in the next 30 years, while the total impact area and the extreme precipitation events in a small range would increase. Under the future scenario, the increase rate of extreme precipitation was highest in August, which increased the probability of extreme events; 3) in the next 30 years, the flood risk had an obvious expansion trend, which was mainly reflected in the expansion of the area of high-, medium-, and low-risk areas. The risk zoning results obtained by the two different flood risk assessment methods were different, but the overall risk trend was the same. This study provided more advanced research for regional flood risk, reasonable prediction for flood risk under future climate models, and useful information for flood disaster prediction in the study area and contributes to the formulation of local disaster prevention and reduction policies.