Quantifying the urbanization effect on station and regional surface air temperature (SAT) trends is a prerequisite for monitoring and detecting long‐term climate change. Based on the data set of satellite visible spectral remote sensing, a new method is developed to determine the urbanization level around observational sites on varied spatial scales and to classify the sites into different categories of stations (U1, U2, …, U6) with U1 the least and U6 the largest affected by urbanization. Urbanization effect on SAT anomaly series of urban and national stations are then evaluated for the periods of 1980–2015 and 1960–2015. Results show that the percentage of built‐up area in different circumferences of the observational sites can be considered as a good indicator of comprehensive urbanization level of station and can be used to classify stations and to determine reference stations; the largest increase in annual mean SAT (T_mean) during 1980–2015 occurred at U6 stations, and U1 stations registered the weakest annual mean warming. The urbanization level is significantly positively correlated to the linear trends of annual mean T_mean and minimum SAT (T_min) and significantly negatively correlated to the diurnal temperature range (DTR) change. The data sets of the national reference climate station network and basic meteorological station network show large urbanization effect and contribution, with the annual mean urbanization contributions reaching 28.7% and 25.8% for the periods 1960–2015 and 1980–2015, respectively. For all the national stations (2,286 in total), the urbanization contributions are 17.1% and 14.6% for the two same periods, respectively.

Whether the urban heat island (UHI) is affected by air pollution in urban areas has attracted much attention. By analyzing the observation data of automatic weather stations and environmental monitoring stations in Beijing from 2016 to 2018, we found a seasonally dependent interlink of the UHI intensity (UHII) and PM_2.5 concentration in urban areas. PM_2.5 pollution weakens the UHII in summer and winter night, but strengthens it during winter daytime. The correlation between the UHI and PM_2.5 concentration has been regulated by the interaction of aerosol with radiation, evaporation and planetary boundary layer (PBL) height. The former two change the surface energy balance via sensible and latent heat fluxes, while the latter affects atmospheric stability and energy exchange. In summer daytime, aerosol‐radiation interaction plays an important role, and the energy balance in urban areas is more sensitive to PM_2.5 concentration than in rural areas, thereby weakening UHII. In winter daytime, aerosol‐PBL interaction is dominant, because aerosols lower the PBL height and stabilize atmosphere, weaken the heat exchange with the surrounding, with more heat accumulated in the urban areas and the increased UHII. Changes in evaporation and radiation strengthen the relationship. At night, the change of UHII more depends on the energy stored in the urban canopy. Aerosols effectively reduce the incident energy during daytime, and the long‐wave radiation from the buildings of urban canopy at night becomes less, leading to a weakened UHII. Our analysis results can improve the understanding of climate‐aerosols interaction in megacities like Beijing.

This paper presents an analysis of changes in global land extreme temperature indices (1951–2015) based on the new global land surface daily air temperature dataset recently developed by the China Meteorological Administration (CMA). The linear trends of the gridpoint time series and global land mean time series were calculated by using a Mann–Kendall method that accounts for the lag-1 autocorrelation in the time series of annual extreme temperature indices. The results, which are generally consistent with previous studies, showed that the global land average annual and seasonal mean extreme temperature indices series all experienced significant long-term changes associated with warming, with cold threshold indices (frost days, icing days, cold nights, and cold days) decreasing, warm threshold indices (summer days, tropical nights, and warm days) increasing, and all absolute indices (TXx, TXn, TNx, and TNn) also increasing, over the last 65 years. The extreme temperature indices series based on daily minimum temperatures generally had a stronger and more significant trend than those based on daily maximum temperatures. The strongest warming occurred after the mid-1970s, and a few extreme temperature indices showed no significant trend over the period from 1951 to the mid-1970s. Most parts of the global land experienced significant warming trends over the period 1951–2015 as a whole, and the largest trends appeared in mid- to high latitudes of the Eurasian continent.