Despite global warming, persistent wet–cold compound extreme events (PWCEs) during wintertime still occur frequently in China, causing significant disruption of human and social activities. However, their full range, precursors, and evolution characteristics remain unclear. In this study, 252 PWCEs in China during cold seasons from 1961 to 2023 are objectively identified and further classified into the South China (SC), Western China (WC), and Central China (CC) types. Although the frequency of PWCEs has decreased under global warming, they still exhibit high extremity, and the WC-type events even show an upward trend in intensity. The three types of PWCE events share a similar “north high–south low” meridional pattern over mid–high latitudes but demonstrate significantly different circulation evolution attributes and precursors: the SC type is dominantly driven by cold-air outbreak linked to the Baikal blocking–East Asian trough; the WC type is controlled by the zonal wave train along the South Asian subtropi-cal westerly jet; the CC type is embedded within a well-organized midlatitude great-circle wave train across Eurasia. Their associated moisture transports also show diverse pathways: the SC type primarily harvests moisture from the South China Sea, the WC type from the India–Burma trough, and the CC type from both the western North Pacific anticyclone and the South China Sea. The precursors for PWCEs originate mainly from the Madden–Julian Oscillation modulating the tropical circulation and moisture transports, especially for the WC and CC types. These findings highlight the diverse formation links and precursors underlying PWCEs in China, offering insights for improving subseasonal prediction of the compound extreme events in China.

Under the global warming, substantial changes have occurred in the intensity and frequency of extreme heat events in China, and accurate prediction of such events is critical to disaster prevention and mitigation. Extreme forecast index (EFI), as an effective method, has been widely used in extreme weather short-range prediction research and operation. This study applies the EFI method to the subseasonal forecasts of summer extreme high temperature in China based on the subseasonal-to-seasonal (S2S) model data from the ECMWF during 2015–2023. The results show that the EFI method can provide skillful predictions of extreme high temperature of surface air at an 8-day lead time in China, with good performance on the subseasonal timescale. Through a new verification formula using normalization, we show that the EFI of high temperature in China has decent prediction skills at 1–19-day lead times, and as lead time extends, the Threat Score (TS) and Factions Skill Score (FSS) decline. In addition, the threshold of extreme temperature has been identified by using a similar normalization method; that is, an observed dimensionless temperature anomaly value of around 1.28, which corresponds to the 90th percentile of the temperature data, can be recognized as an extreme high temperature. The effectiveness of the EFI verification and threshold identification approaches shed lights on the improvement for subseasonal prediction of extreme high temperature in China.

Based on a combination of six Chinese climate models and three international operational models, the China multi-model ensemble (CMME) prediction system has been upgraded into its version 2 (CMMEv2.0) at the National Climate Centre (NCC) of the China Meteorological Administration (CMA) by including new model members and expanding prediction products. A comprehensive assessment of the performance of the upgraded CMME during its hindcast (1993–2016) and real-time prediction (2021–present) periods is conducted in this study. The results demonstrate that CMMEv2.0 outperforms all the individual models by capturing more realistic equatorial sea surface temperature (SST) variability. It exhibits better prediction skills for precipitation and 2-m temperature anomalies, and the improvements in prediction skill of CMMEv2.0 are significant over East Asia. The superiority of CMMEv2.0 can be attributed to its better projection of El Niño–Southern Oscillation (ENSO; with the temporal correlation coefficient score for Niño3.4 index reaching 0.87 at 6-month lead) and ENSO-related teleconnections. As for the real-time prediction in recent three years, CMMEv2.0 has also yielded relatively stable skills; it successfully predicted the primary rainbelt over northern China in summers of 2021–2023 and the warm conditions in winters of 2022/2023. Beyond that, ensemble sampling experiments indicate that the CMMEv2.0 skills become saturated after the ensemble model number increased to 5–6, indicating that selection of only an optimal subgroup of ensemble models could benefit the prediction performance, especially over the extratropics, yet the underlying reasons await future investigation.