Using CN05.1 observations and high-resolution NEX-GDDP-CMIP6 simulations, we evaluate the performance of climate models in reproducing extreme precipitation over the Yangtze River Basin based on Taylor diagrams, Taylor Skill Score, and Interannual Variability Score. Extreme Value Theory (EVT), through the Generalized Extreme Value (GEV) distribution, is further applied to estimate the 10-year and 20-year return levels of RX1day and RX5day. On this basis, we analyse the future evolution of extreme precipitation under different scenarios and quantify changes in population and GDP exposure. The results show that NEX-GDDP-CMIP6 and its multi-model ensemble mean reproduce the observed spatial and temporal patterns of extreme precipitation reasonably well, with the ensemble generally outperforming individual models. Across future scenarios, basin-mean values of the eight extreme precipitation indices exhibit significant upward trends, indicating a robust increase in the frequency and intensity of extreme events. By combining GEV-based return levels with population and GDP projections, we find that socioeconomic exposure to extreme precipitation increases substantially throughout the twenty-first century. Contribution analysis reveals that changes in population exposure are dominated by climate effects, whereas changes in GDP exposure arise from the combined influence of climate change and economic growth.

Accurate characterization of precipitation in complex terrain is essential for hydrological modeling and climate studies. This study uses daily observations from 156 rain gauges in Sichuan Province (2015–2020) to evaluate two high-resolution satellite products (GSMaP-GNRT and IMERG-Early) and to develop a Transformer-based fusion framework at the gauge scale. All three datasets reproduce the regional seasonal cycle with more rainfall in summer and less in winter. At the daily scale, the fused product attains correlation comparable to GSMaP, while GSMaP and the fusion slightly overestimate precipitation (Bias = 6.24% and 5.21%), and IMERG shows stronger underestimation (Bias = −11.46%). At the monthly scale, the fused dataset achieves the best overall performance in terms of correlation, bias and RMSE. Spatially, the fusion reduces bias and RMSE and yields more homogeneous patterns over Sichuan’s complex terrain. Detection metrics indicate that the fused product increases the probability of detection and slightly improves the critical success index, while the false alarm ratio remains relatively high and comparable to the original products. This implies a gain in event sensitivity and spatial consistency rather than substantially reduced false alarms. Overall, the Transformer-based fusion provides a useful compromise between GSMaP and IMERG, adding value particularly for bias reduction, monthly statistics and event detection. The fused dataset offers a promising input for precipitation monitoring, hydrological simulation and disaster-risk analysis in Sichuan and similar mountainous regions.