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.

Owing to limited observations, it remains unknown whether the impact of El Niño–Southern Oscillation (ENSO) on the Indian Ocean–Northwest Pacific (IO–NWP) climate showed decadal changes in the early 20th century. Using multi-source reanalysis and hindcast datasets from the ECMWF and NOAA extending back to 1901, this study investigates interdecadal variations of the impact of ENSO on the IO–NWP climate from 1901 to 2009. It is found that the influence of ENSO on the IO–NWP climate shows “strong–weak–strong” interdecadal change during 1901–2009. This is characterized by much weaker Indian Ocean sea surface temperature (SST) warming and a weaker NWP subtropical anticyclone (NWPSA) in the following summer of El Niño during 1946–1967, compared with those in the other two periods (1901–1945 and 1968–2009). Analyses of the datasets indicate that the interdecadal variation is mainly associated with the change in ENSO amplitude. In contrast to the period of 1946–1967, a greater SST variance occurred in the central–eastern equatorial Pacific during 1901–1945 and 1968–2009. A stronger El Niño tends to generate more significant anticyclonic anomalies over the southeast Indian Ocean through teleconnection. The northwesterly anomalies to the south of the anticyclone weaken the southeast trade winds and warm the south Indian Ocean SST via wind–evaporation–SST feedback, and the positive south Indian Ocean SST anomalies trigger westward-propagating oceanic Rossby waves to induce stronger warming of the southwest Indian Ocean, leading to a significant asymmetric wind pattern across the equator in spring. The profound northeastward winds on the north side weaken the southwest monsoon, leading to a “second warming” over the north Indian Ocean in summer, which anchors the eastward-propagating warm Kelvin waves and results in a stronger NWPSA by inducing surface divergence and suppressing deep convection.