Heat waves, particularly humid heat waves that occur together with high temperatures and humidity, pose a severe hazard to human health in the context of global warming. In this work, daily observations from more 1500 sites are used to define dry and humid heat waves in a warm season (May to September) and assess their spatiotemporal characteristics during 1960–2015. Dry heat waves are identified with daily maximum temperatures, while humid heat waves are identified based on the wet bulb temperature, derived from both temperature and humidity. We compare dry and humid heat waves from various aspects based on multiple heat wave indices, including their frequency, duration, and intensity. Results suggest that the occurrence of both dry and humid heat wave days in China is higher in the southern than in the northern part of China, due to the higher air temperature and humidity therein. Compared to dry heat waves, humid heat waves are of higher amplitude but shorter duration. The long-term trend analysis shows that the occurrence of both dry and humid heat waves has increased overall over the last 50 years, with an especially rapid increase in the last 20 years, which may be related to China’s rapid warming since the late 1980s. The future projections with multiple global climate models indicate that China will experience more frequent, stronger, and longer-duration dry and humid heat waves in the future, under both intermediate and high-emission pathways.

Understanding the physical mechanisms of compound hot-drought events (CHDEs) over the Yangtze River Basin (YRB) is essential for improving climate predictability. Using high-resolution observations and ERA5 reanalysis, this study conducted a three-dimensional comparative diagnosis of two spatially distinct CHDEs (August 2022 and 2019), deconstructing their circulation dynamics and thermodynamic budgets. Specifically, the 2022 basin-wide event was highly associated with La Niña and a negative Indian Ocean Dipole (NIOD). Anomalous latent heating in the tropical eastern Indian Ocean favored an extensive meridional Hadley circulation and a deep high-pressure belt, blocking southwest moisture transport. Thermodynamically, intense downward vertical motions produced severe descending adiabatic warming, which offset longwave radiational cooling and sustained anomalous heat accumulation throughout the deep troposphere. Conversely, the 2019 localized event occurred under a weak Central Pacific El Niño and a positive IOD. The anomalous tropical cooling weakened systematic subsidence and provided a favorable background for a Rossby wave train, featuring an offshore cyclone over the Western North Pacific that severed moisture pathways. Characterized by shallower vertical subsidence, the lack of a penetrative adiabatic heat source merely maintained a lower-level thermodynamic balance, confining extreme heat to the central–eastern YRB. Although superficially similar at the surface, these CHDEs are sustained by two distinct ocean–atmosphere interaction paradigms. Clarifying these dual paradigms provides a critical physical basis for improving sub-seasonal prediction.

During the winter of 2023-2024, Hunan province experienced four rounds of freezing disaster weather processes (Dec 15-20 ^th in 2023, Jan 18-25 ^th , Feb 1-11 ^th , and Feb 20-29 ^th in 2024). The maximum wind power de-rated capacity due to icing in the province reached 7850 MW (on Feb 25 in 2024), accounting for 82.3% of the total installed wind power capacity in Hunan province. Using a wind turbine icing and standby withdrawal prediction model, the average prediction accuracy rates for turbine icing de-rating across the four rounds were 83.8%, 78.4%, 83.3%, and 92.2%, respectively. Further error analysis of the prediction results revealed that the average temperature predicted is higher than the actual temperature, which may be the reason for the prediction error of wind power capacity. Therefore, using the Qiujialun wind farm as a sample, which had the largest prediction error, a temperature correction algorithm for individual turbines was established. The meteorological data at the model layer closest to the actual turbine hub altitude are used to reduce the deviation between the model altitude and the actual altitude in this algorithm, which is critical for mountainous wind farms like those in Hunan province. The improved algorithm was applied during the third and fourth rounds. The average absolute deviation (AAD) of the de-rating prediction for Qiujialun decreased from 22~24.1 MW in the first two rounds to 4.7~7.5 MW in the last two rounds, indicating improved prediction accuracy. Based on this correction method, recalculations were performed for the first two rounds. The absolute deviation for the first round decreased from 22 MW to 11.9 MW, and for the second round from 24.1 MW to 13.8 MW, representing an error reduction of approximately 50%.

The station observations and reanalysis dataset are utilized to explore the effects of Madden-Julian Oscillation (MJO) on the cold extremes over the whole country of China, and the possible mechanisms from the perspective of the thermodynamics. Here, we focus on the principal modes of different phases of MJO in winter and their influences on the large scale cold events (LSCEs) that are identified firstly. The time evolution and the spatial features of LSCEs are checked for a basic insight of the LSCEs in China, of which the annual variability and regional differences are obvious among the chosen LSECs. In addition, the first (second) empirical orthogonal decomposition mode of MJO shows an opposite feature that positive values in Phases 1–3 (Phase 4–5) and negative values in Phases 5–7 (Phase 7–8), with the explanation of variance at 30.6% (26%). Furthermore, according to the threshold of ±1.5 in standardized time series of two principal components (PC1 and PC2), the events are chosen and clarified into four cases (+PC1, –PC1, +PC2 and –PC2). All the MJO-related cases present the increases of LSCEs but with regional and intensity differences. For the case of +PC1, the cold advection from higher latitudes transport to eastern Asia inducing negative temperature anomalies thereof. For the case of –PC1, besides the eastern Asian region, there still the cold advections across the Inner Mongolia regions, leaving negative anomalies over the region either. For the case of +PC2, the southward wind and the accompanied cold advection are stronger than the others affecting mostly regions in China, which leads to the more decreases of temperature. For the case of –PC2, the cold advections are weaker, resulting in the less temperature decreases over the southeastern China. Meanwhile, the tropospheric cyclonic and anti-cyclonic circulation anomalies are beneficial to the persistence of local extremes.