This study evaluates the performance of 15 Coupled Model Intercomparison Project Phase 6 (CMIP6) models (before and after downscaling) in simulating autumn precipitation extremes in Southwest China based on a high-resolution, statistically downscaled CMIP6 dataset, using the CN05.1 dataset as a reference. The Bias Correction Constructed Analogues with Quantile mapping reordering (BCCAQ) method used in deriving the downscaled CMIP6 dataset significantly enhances the models’ abilities to reproduce the spatial patterns of the extreme precipitation indices, particularly for total precipitation, number of moderate rain days (R10), and number of heavy rain days (R20). Notable improvements are also observed for maximum 1-day precipitation (RX1), maximum 5-day precipitation (RX5), and simple daily intensity index (SDII), alongside reduced inter-model spread and systematic biases. Bias correction also improves the simulation of interannual variability, substantially reducing the root mean square error (RMSE) for total precipitation, R10, and R20. Increased interannual variability in the future is expected for certain indices, spatially concentrated for RX1 and RX5 in the south and R20 in the east. Projections using the bias-corrected multi-model ensemble under the SSP2-4.5 and SSP5-8.5 scenarios indicate a significant intensification of autumn extreme precipitation in both intensity- and frequency-related indices by the 2080s, especially in southern Southwest China, with precipitation becoming more concentrated in heavier events. Consecutive dry days (CDDs) exhibit spatial variability with an observed increase in the southeast, while consecutive wet days (CWDs) shows no significant change. These findings highlight an increased risk of intensified autumn rainfall and altered precipitation patterns in the region under future climate change.

Based on the recently released NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) Coupled Model Intercomparison Project Phase 6 (CMIP6) dataset and the gridded observational daily dataset CN05.1, this study evaluates the performance of 26 CMIP6 models in simulating extreme high temperature (EHT) indices in southwestern China and estimates future changes in the EHT indices under the Shared Socioeconomic Pathways (SSPs) SSP1-2.6, SSP2-4.5, and SSP5-8.5 using 11 optimal CMIP6 models. Five EHT indices are employed: annual maximum value of daily maximum temperature (TXX), high temperature days (T35), warm days (TX90P), heat wave frequency (HWF), and heat wave days (HWD). The main results are as follows. (1) NEX-GDDP-CMIP6 is highly capable of simulating the spatial patterns of TXX and T35 in southwestern China but it presents a weaker ability to simulate the spatial patterns of TX90P, HWF, and HWD. (2) The simulated time series of T35, TX90P, HWF, and HWD in southwestern China exhibit consistent upward trends with the observations. The linear trends of increase in TX90P and HWD are much greater than those of increase in TXX, T35, and HWF. (3) The estimated increases in TXX and T35 in southwestern China are significantly greater in Chongqing and the adjacent areas of Sichuan than in the other regions. Spatial distributions of the increases in TX90P, HWF, and HWD generally show higher values in the west and lower values in the east. (4) In the three different scenarios, the projected future TXX, T35, TX90P, and HWD in southwestern China all display a continuous increase with time and radiative forcing levels, whereas HWF initially increases but then decreases under the SSP5-8.5 scenario. By the end of the 21st century, under the SSP5-8.5 scenario, TXX and T35 are projected to increase by 6.0°C and 45.0 days, respectively. The duration of individual heat waves is also expected to increase.