Reproducing the Madden‐Julian Oscillation (MJO) remains a challenge in many climate models. This study compares the MJO simulation skills in 17 pairs of Coupled Model Intercomparison Project Phase 5 (CMIP5) and Coupled Model Intercomparison Project Phase 6 (CMIP6) simulations. Generally, the MJO characteristics are better reproduced in CMIP6, their inter‐model spreads are obviously reduced in CMIP6. However, most CMIP6 models still underestimate the frequency of MJO initiation over the Indian Ocean and the MJO amplitude, exaggerate the barrier effect of the Maritime Continent (MC) on MJO propagation. Models with lower MJO simulation skills in their CMIP5 versions show improvements in CMIP6, while other models tend to show degraded MJO simulation skills in CMIP6. The possible reasons for the apparent improvements of MJO simulations from CMIP5 to CMIP6 in six models was further explored. It was found that the enhanced land precipitation over the MC prior to the MJO convection entering this region is more obvious in CMIP6.

Long-lived (≥6 days) heatwaves (HWs) have strong social impacts with serious health implications. Using homogenized historical daily temperatures from China and ECMWF reanalysis data, this study investigates its frequency between 1979 and 2018 and driving mechanisms. It is found that the occurrence of HWs is strongly associated with the joint actions of the South Asian high and the western North Pacific subtropical high, which can be described by a synergy index measured by the boundary distance between the two subtropical high-pressure systems. When the synergy index is positive, there are more long-lived HWs occurrence in the east of the Tibetan Plateau, the lower reaches of the Yangtze River and the southern region in China, and vice versa. A Mann-Kendall test shows a significant interdecadal shift around 2004/2005 towards increased occurrence that is consistent with enhanced subtropical high systems. This study shows the important roles of large-scale dynamic systems in regional climate extremes and their future changes.

Wind energy is a fluctuating source for power systems, which poses challenges to grid planning for the wind power industry. To improve the short-term wind forecasts at turbine height, the bias correction approach Kalman filter (KF) is applied to 72-h wind speed forecasts from the WRF model in Zhangbei wind farm for a period over two years. The KF approach shows a remarkable ability in improving the raw forecasts by decreasing the root-mean-square error by 16% from 3.58 to 3.01 m s⁻¹, the mean absolute error by 14% from 2.71 to 2.34 m s⁻¹, the bias from 0.22 to − 0.19 m s⁻¹, and improving the correlation from 0.58 to 0.66. The KF significantly reduces random errors of the model, showing the capability to deal with the forecast errors associated with physical processes which cannot be accurately handled by the numerical model. In addition, the improvement of the bias correction is larger for wind speeds sensitive to wind power generation. So the KF approach is suitable for short-term wind power prediction.