Research on climate change projections aims to provide decision-makers with more reliable and less uncertain information about future climate change. This paper reviews the major progress made in China over the past decade regarding climate change projections, and discusses future perspectives in this field. Climate model projections indicate that both regional average temperatures and precipitation in China will increase, with the largest increases occurring under the scenarios of the highest emissions. In the future, extreme cold events in China are expected to decrease, while extreme heat events will become more frequent; extreme precipitation will continue to rise significantly in intensity and frequency; and compound extreme events will also see a notable increase, in particular the rarest extreme events, which will rise more significantly. Statistical bias-calibration, model weighting, constraint based on detection and attribution, and emergent constraint have been widely applied in regional climate change projections in China. Overall, constrained projections do not alter the qualitative conclusions of the raw model projections, but adjust the magnitude of the projected change. The observational constraint methods have demonstrated the ability to reduce uncertainty in projections across different regions and variables in China. To further advance the regional climate change projection research in China, it is essential to deepen understanding of the climate system and its feedback processes, improve the quality of observational data and the performance of climate model simulations, and enhance the application of emerging technologies such as machine learning.

Research on climate change projections aims to provide decision-makers with more reliable and less uncertain information about future climate change. This paper reviews the major progress made in China over the past decade regarding climate change projections, and discusses future perspectives in this field. Climate model projections indicate that both regional average temperatures and precipitation in China will increase, with the largest increases occurring under the scenarios of the highest emissions. In the future, extreme cold events in China are expected to decrease, while extreme heat events will become more frequent; extreme precipitation will continue to rise significantly in intensity and frequency; and compound extreme events will also see a notable increase, in particular the rarest extreme events, which will rise more significantly. Statistical bias-calibration, model weighting, constraint based on detection and attribution, and emergent constraint have been widely applied in regional climate change projections in China. Overall, constrained projections do not alter the qualitative conclusions of the raw model projections, but adjust the magnitude of the projected change. The observational constraint methods have demonstrated the ability to reduce uncertainty in projections across different regions and variables in China. To further advance the regional climate change projection research in China, it is essential to deepen understanding of the climate system and its feedback processes, improve the quality of observational data and the performance of climate model simulations, and enhance the application of emerging technologies such as machine learning.

Meiyu, featuring prolonged periods of rainfall over the Yangtze–Huai River basin (YHRB), not only replenishes water resources and sustains ecological balance, but also poses potential disaster risks. Accurate early forecasting of Meiyu is crucial for effectively implementing flood prevention strategies. To help refine numerical models and providing guidance for operational forecasters, this study explores the capabilities of two global ensemble prediction systems (GEPSs) of the China Meteorological Administration (CMA) and the ECMWF in forecasting the Meiyu characteristics in 2023 over the YHRB. Results show that the ECMWF GEPS reasonably forecasts the Meiyu rainfall, while the CMA GEPS presents a notable underestimation. The predictable lead time of the Meiyu onset date is eight days by the ECMWF GEPS and six days by the CMA GEPS, respectively. Regarding the regional rainstorm processes, the two GEPSs generally provide a predictable lead time of 48–168 h for reasonably forecasting the patterns of the heavy rainfall area. To further examine their strengths and weaknesses in Meiyu forecasting, this paper revisits their abilities in forecasting key influence systems. By verifying against their respective analyses, it is demonstrated that the ECMWF GEPS reasonably forecasts the spatial coverages of the northwestern Pacific subtropical high (NWPSH) and the South Asian high (SAH), whereas the CMA GEPS presents substantial underestimation. Both GEPSs show generally southward deviations for the eastern ridge line position (RLP) of the SAH, and exhibit a northward deviation for the western RLP of the NWPSH during early forecast lead times. The less Meiyu rainfall predicted by the CMA GEPS compared to the ECMWF GEPS can be attributed to its weaker low-level convergence belt and weaker upper-level divergence area. A deeper exploration into these forecast discrepancies in upper-level divergence and lower-level convergence suggests that they likely originate from their initial analysis fields.