In this study, based on total column ozone observations from eight Antarctic stations, we evaluate the applicability of ERA5, C3S-MSR, MERRA-2, and JRA-55 reanalysis datasets and the NIWA-BS merged satellite dataset, in terms of interannual variation and long-term trend, using the correlation coefficient (R), root-mean-square error (RMSE), interannual variability skill score (IVS), and linear trend bias (TrBias). The results show that for interannual variation, C3S-MSR performs well at multiple stations, while JRA-55 performs poorly at most stations, especially Marambio, Rothera, and Faraday/Vernadsky, which are located at lower latitudes on the Antarctic Peninsula. Additionally, all datasets show significantly higher RMSE at Dumont D’Urville and Arrival Heights, which generally are located around the edge of the Antarctic stratospheric vortex where total column ozone values are more variable and on average larger than in the core of the vortex. The comprehensive ranking results show that C3S-MSR performs the best, followed by ERA5 and NIWA-BS, with MERRA-2 and JRA-55 ranking lower. For the long-term trend, each of the datasets has large bias values at Arrival Heights, and the absolute TrBias values of JRA-55 are larger at three stations on the Antarctic Peninsula. The overall averaged results show that C3S-MSR and NIWA-BS have the smallest absolute TrBias, and perform best in reflecting the Antarctic ozone trends, while ERA5 and JRA-55 significantly overestimate the Antarctic ozone recovery trend and perform poorly. Based on our analysis, the C3S-MSR dataset can be recommended to be prioritized when analyzing the interannual variations in Antarctic stratospheric ozone, and both the C3S-MSR reanalysis and NIWA-BS datasets should be prioritized for trend analysis.

Anthropogenic emissions have caused the Antarctic ozone hole, a major global environmental crisis since the late 20th century. Although ozone recovery began in the early 21st century, substantial uncertainty remains regarding the timing of its return to pre-loss levels. This study innovatively develops a “model optimization–bias correction” framework based on spatial pattern (S1) and long-term trend (S2) metrics, assessing 17 Chemistry-Climate Model Initiative Phase 1 (CCMI-1) models using the fifth generation of the European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis for the climate (ERA5). Results: (1) Most models accurately reproduce the Antarctic ozone’s spatial distribution and long-term trends: MRI-ESM1r1 performs best for spatial patterns (S1 = 0.80), GEOSCCM for long-term trends (S2 = 0.82); EMAC-L90MA, UMSLIMCAT, etc., show poor spatial pattern performance (S1 < 0.30), while IPSL and EMAC-L90MA have large trend biases and underperform in trend simulation (S2 < 0.10). (2) Integrating S1 and S2 scores, the Preferred Multi-Model Ensemble comprising the top eight models (PMME8) minimizes ERA5 deviation, outperforming the multi-model ensemble (MME); the Combined Nonstationary Cumulative Distribution Function matching (CNCDFm) correction of this ensemble reduces systematic bias by 15–60%. (3) Antarctic ozone recovery time shows a gradual delay following optimal model selection and bias correction. PMME-adjusted projects recovery in October 2063 (2053–2072), later than MME (2052) and PMME (2058), with inter-member uncertainty narrowing from 43 years to 19 years. Similarly, this feature is also found for September, November, and the spring mean. This study provides a reliable methodological foundation for projections of Antarctic ozone recovery and offers scientific support for the compliance assessment and policy adjustment of the Montreal Protocol, thereby advancing environmental sustainability and global ozone governance.

Global warming has intensified extreme heat events, posing growing climate risks. As a climate-sensitive region, China has experienced compound daytime and nighttime extreme heat, threatening health, ecosystems, and socioeconomic development. Accurate projections of future extreme heat in China under different emission scenarios are therefore essential for climate risk assessment, adaptation planning, and regional sustainable development. This study integrates the SimmEX extreme temperature index dataset and NEX–GDDP–CMIP6 climate model data to investigate six daytime and nighttime extreme temperature indices (TXx, TNx, TX90p, TN90p, SU, and TR). A spatiotemporal evaluation framework is developed to optimize the Multi-Model Ensemble (MME), and the Combined Nonstationary Cumulative Distribution Function matching (CNCDFm) method is applied for bias correction. The main findings are as follows: (1) CMIP6 models can generally reproduce the spatiotemporal characteristics of extreme heat over China. Intensity-based indices (TXx, TNx) showed the highest skill (spatial correlations 0.85–0.95), followed by absolute-threshold indices (SU, TR), while percentile-based indices (TX90p, TN90p) exhibited larger uncertainties. (2) MME performance did not improve monotonically with increasing ensemble size. The spatiotemporal evaluation framework identified optimal models for different indices, and the corresponding optimal MME sizes balancing accuracy and robustness were identified as 7 for TXx, 4 for TNx, 5 for TX90p, 4 for TN90p, 4 for SU, and 4 for TR. (3) CNCDFm improved MME–observation agreement, with greater improvements for TXx, TNx, SU, and TR than for the percentile-based TX90p and TN90p. Extreme heat is projected to increase across China, with stronger increases under higher-emission scenarios and distinct regional patterns among different indices. By the late 21^st century, TXx and TNx are projected to rise by 6.7 °C and 7.3 °C under SSP5-8.5, compared with 2.4 °C and 3.1 °C under SSP1-2.6. Nighttime indices exhibit stronger increases than daytime indices, highlighting greater extreme heat risks under higher emissions.