Heat waves and urban heat islands (UHIs) may interact together, but the dependence of their interaction on background climate is unclear. Hourly meteorological observations in June to August from 2013 to 2015 collected in the megacities of Beijing (temperate semihumid monsoon climate), Shanghai (subtropical humid monsoon climate), and Guangzhou (marine subtropical monsoon climate) in China were used to study the interaction. At each megacity, eight rural stations and eight urban stations, respectively, were selected to study the UHI. Although under different background climates, UHIs in Beijing and Guangzhou shared a similar diurnal variability, that is, higher in the nighttime. However, the diurnal cycle is opposite for Shanghai if rural coastal stations were selected as rural reference stations. During heat wave periods, daytime (10:00–16:00) UHIs were intensified by 0.9 ± 0.13 (mean ± 1 standard deviation) °C in Shanghai, nighttime (22:00–4:00) UHIs were intensified by 0.9 ± 0.36 and 0.8 ± 0.20 °C in Beijing and Guangzhou, respectively. The surface solar radiation during the heat wave period was approximately 1.5 times to that under normal conditions in each city. The enhanced solar radiation during heat waves, which was absorbed by the urban canopy in the daytime and released at night, was closely related to nighttime UHIs in Beijing and Guangzhou and daytime UHIs in Shanghai. Additionally, changes in wind direction were observed in Shanghai under heat waves, that is, with more than 63% (wind direction) of the wind originating from neighboring hot cities in the southwest instead of the cool sea breeze from the southeast, which led to a significant increase in daytime UHIs during heat wave periods.

This study compared the diurnal and seasonal cycles of atmospheric and surface urban heat islands (UHIs) based on hourly air temperatures (T_a) collected at 65 out of 262 stations in Beijing and land surface temperature (T_s) derived from Moderate Resolution Imaging Spectroradiometer in the years 2013–2014. We found that the nighttime atmospheric and surface UHIs referenced to rural cropland stations exhibited significant seasonal cycles, with the highest in winter. However, the seasonal variations in the nighttime UHIs referenced to mountainous forest stations were negligible, because mountainous forests have a higher nighttime T_s in winter and a lower nighttime T_a in summer than rural croplands. Daytime surface UHIs showed strong seasonal cycles, with the highest in summer. The daytime atmospheric UHIs exhibited a similar but less seasonal cycle under clear‐sky conditions, which was not apparent under cloudy‐sky conditions. Atmospheric UHIs in urban parks were higher in daytime. Nighttime atmospheric UHIs are influenced by energy stored in urban materials during daytime and released during nighttime. The stronger anthropogenic heat release in winter causes atmospheric UHIs to increase with time during winter nights, but decrease with time during summer nights. The percentage of impervious surfaces is responsible for 49%–54% of the nighttime atmospheric UHI variability and 31%–38% of the daytime surface UHI variability. However, the nighttime surface UHI was nearly uncorrelated with the percentage of impervious surfaces around the urban stations.

Evaluations of the impacts of increasing urbanization ratios on air temperatures are less quantitative and accurate due to the lack of high‐resolution land cover data sets. Here, based on the latest released high‐resolution (30‐m) land cover data from 1985 to 2017, annual urbanization ratios around each station in Beijing were calculated. Warming rates and temperature extremes were compared between nonrural stations (17 stations with continuously increasing urbanization ratios) and purely rural stations (3 stations with continuous urbanization ratios of zero). Although urbanized areas only occupied 22.2% of the total area in Beijing, urbanization ratios at nonrural stations already reached an average of 62.4% (10 km²)⁻¹in 2017, and their average rate reached 15.1% (10 km²)⁻¹ decade⁻¹. From 1985 to 2017, each 10% increase in the urbanization ratio with 10 km²had led to additional annual warming of 0.14 ± 0.11, 0.17 ± 0.08, and 0.30 ± 0.17°C at nonrural stations for daily maximum (T_max), mean (T_mean), and minimum (T_min) air temperatures, respectively. Meanwhile, each 10% increase in the urbanization ratio with 10 km²had led to additional changes of 11.6 ± 5.1, 12.7 ± 5.8, and −11.1 ± 5.2 days at nonrural stations for hot days, hot nights, and chilly nights, respectively. Additionally, the inner‐annual urbanization contributions to the warming increased significantly at rates of 10.7% decade⁻¹and 14.1% decade⁻¹forT_meanandT_min, and reached 49.0% and 52.6% in 2017, respectively. The inner‐annual urbanization contributions to the increasing hot nights and the decreasing chilly nights changed at rates of 16.7% decade⁻¹and 15.6% decade⁻¹, and reached 57.8% and 53.1% in 2017, respectively.

Hourly meteorological data and multisource socioeconomic data collected in the Yangtze River Delta (YRD) region were used to analyze its heat vulnerability during the record-breaking hot summer of 2022 in both daytime and nighttime. Over forty consecutive days, daytime temperatures exceeded 40 °C, and 58.4% of the YRD region experienced 400 h with temperatures hotter than 26 °C during the nighttime. Only 7.5% of the YRD region was under low heat risk during both daytime and nighttime. Strong heat risk combined with strong heat sensitivity and weak heat adaptability led to strong heat vulnerability during both daytime and nighttime in most areas (72.6%). Inhomogeneity in heat sensitivity and heat adaptability further aggravated the heterogeneity of heat vulnerability, leading to compound heat vulnerability in most regions. The ratios of heat-vulnerable areas generated by multiple causes were 67.7% and 79.3% during daytime and nighttime, respectively. For Zhejiang and Shanghai, projects designed to decrease the urban heat island effect and lower the local heat sensitivity are most important. For Jiangsu and Anhui, measures aiming to decrease the urban heat island effect and improve heat adaptability are most important. It is urgent to take efficient measures to address heat vulnerability during both daytime and nighttime.