The characteristics of heat waves (HWs) in southern China in 1979–2010 are studied by using both reanalysis and station datasets. Guangdong Province of China (GDPC) is taken as an example. It is found that the westward movement of the western North Pacific subtropical high (WNPSH) is the primary factor for large-scale HWs occurring in GDPC. When an HW occurs, a hot and dry atmospheric column prevails over southern China. The region is overlaid by anomalous subsidence, which leads to warming, and clear sky, which causes greater solar heating. HWs are accompanied by an anomalous high pressure center and anticyclone near the surface, with anomalous land–sea northwesterly flow, thus reducing sea–land moisture transport and drying the atmosphere over land. The evolution of the high pressure anomaly and high temperature is associated with the westward displacement of WNPSH, with a prominent positive anomaly in 500-hPa height migrating westward. All these features associated with HWs in GDPC coincide with high-temperature extremes in the whole region of southern China and parts of Indochina. Significant increases in HW frequency (+0.19 events decade⁻¹), HW days (+2.86 days decade⁻¹), the duration of the longest event (+0.38 days decade⁻¹), and the hottest temperature of the hottest event (+0.23°C decade⁻¹) are also observed. These upward trends are more prominent in the Pearl River delta (PRD) region, and urbanization contributes to nearly 50% of the increase in HW frequency in PRD. It is also noticed that HWs are commencing earlier and ending later, and urbanization may advance the timing of the onset of HW events.

A combination of hot temperature and high humidity (high heat stress) has severe impacts on environment, society, and public health, especially in urban areas where the majority of the world's population lives. This study investigates the changes of heat stress in urban areas of eastern China and urbanization effects. Data for 242 urban areas and records from a dense network of nearly 2,000 stations are examined. All urban areas have experienced substantial increases in mean heat stress and the frequencies of extreme heat stress days and events during 1971–2014. The increases in human‐perceived heat stress are even stronger than air temperature. Urban areas experience more intense heat stress than the surrounding rural areas. We estimate that urbanization accounts for nearly 30% of the increase in mean and extreme heat stress. Urbanization effects are more prominent in the major urban conglomerates such as Beijing‐Tianjin‐Hebei and the Yangtze and Pearl river deltas.

It is well known that urban areas are typically hotter than the surrounding (vegetated) rural areas. However, the contribution of urbanization to the trends of extreme temperature events such as heat waves (HWs) is less understood. Using a homogenized meteorological dataset drawn from nearly 2,000 stations in China, we find that urban and rural areas have different HW trends and the urban‐rural contrast of HW trends varies across climate regimes. In wet climates, the increasing trends of HWs in urban areas are greater than those in rural areas, suggesting a positive contribution of urbanization to HW trends. In arid regions, the urbanization contribution to HW trends is smaller and even negative. The stronger urbanization contribution to HW trends in wet climates is linked to the smaller variability of urban heat island intensity. This study highlights the important role of local hydroclimate in modulating the urbanization contribution to extreme temperatures.

With increasing public concerns on air pollution in China, there is a demand for long-term continuous PM2.5 datasets. However, it was not until the end of 2012 that China established a national PM2.5 observation network. Before that, satellite-retrieved aerosol optical depth (AOD) was frequently used as a primary predictor to estimate surface PM2.5. Nevertheless, satellite-retrieved AOD often encounter incomplete daily coverage due to its sampling frequency and interferences from cloud, which greatly affect the representation of these AOD-based PM2.5. Here, we constructed a virtual ground-based PM2.5 observation network at 1180 meteorological sites across China using the Extreme Gradient Boosting (XGBoost) model with high-density meteorological observations as major predictors. Cross-validation of the XGBoost model showed strong robustness and high accuracy in its estimation of the daily (monthly) PM2.5 across China in 2018, with R2, root-mean-square error (RMSE) and mean absolute error values of 0.79 (0.92), 15.75 μg/m3 (6.75 μg/m3) and 9.89 μg/m3 (4.53 μg/m3), respectively. Meanwhile, we find that surface visibility plays the dominant role in terms of the relative importance of variables in the XGBoost model, accounting for 39.3% of the overall importance. We then use meteorological and PM2.5 data in the year 2017 to assess the predictive capability of the model. Results showed that the XGBoost model is capable to accurately hindcast historical PM2.5 at monthly (R2 = 0.80, RMSE = 14.75 μg/m3), seasonal (R2 = 0.86, RMSE = 12.28 μg/m3), and annual (R2 = 0.81, RMSE = 10.10 μg/m3) mean levels. In general, the newly constructed virtual PM2.5 observation network based on high-density surface meteorological observations using the Extreme Gradient Boosting model shows great potential in reconstructing historical PM2.5 at ~1000 meteorological sites across China. It will be of benefit to filling gaps in AOD-based PM2.5 data, as well as to other environmental studies including epidemiology.

Urban land expansion is one of the most conspicuous aspects of urbanization and has profound impacts on regional climate change. Most studies, however, focus on its impacts on surface temperature, and possible effects on atmospheric humidity are less known. By examining the humidity changes in the Yangtze River Delta urban agglomeration of China during 1961–2014, we find a prominent urban dry island (UDI) effect in this urban agglomeration, as characterized by reduced humidity and increased vapor pressure deficit in the urban core area. In the past decades, the UDI effect has been significantly intensified by rapid urban land expansion. Urban expansion contributes to around half of the decrease in atmospheric humidity and the increase in vapor pressure deficit in urban areas. These effects are particularly stronger in wet hot summer and relatively weaker in cold dry winter. We suggest that the UDI effect should be considered in future urban planning, landscape design, and climate change assessment and mitigation.