Meteorologically observed air temperature (T_a) is limited due to low density and uneven distribution that leads to uncertain accuracy. Therefore, remote sensing data have been widely used to estimate near-surface T_a on various temporal scales due to their spatially continuous characteristics. However, few studies have focused on instantaneous T_a when satellites overpass. This study aims to produce both daily and instantaneous T_a datasets at 1 km resolution for the Jingjinji area, China during 2018–2019, using machine learning methods based on remote sensing data, dense meteorological observation station data, and auxiliary data (such as elevation and normalized difference vegetation index). Newly released Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 surface Downward Shortwave Radiation (DSR) was introduced to improve the accuracy of T_a estimation. Five machine learning algorithms were implemented and compared so that the optimal one could be selected. The random forest (RF) algorithm outperformed the others (such as decision tree, feedforward neural network, generalized linear model) and RF obtained the highest accuracy in model validation with a daily root mean square error (RMSE) of 1.29 °C, mean absolute error (MAE) of 0.94 °C, daytime instantaneous RMSE of 1.88 °C, MAE of 1.35 °C, nighttime instantaneous RMSE of 2.47 °C, and MAE of 1.83 °C. The corresponding R² was 0.99 for daily average, 0.98 for daytime instantaneous, and 0.95 for nighttime instantaneous. Analysis showed that land surface temperature (LST) was the most important factor contributing to model accuracy, followed by solar declination and DSR, which implied that DSR should be prioritized when estimating T_a. Particularly, these results outperformed most models presented in previous studies. These findings suggested that RF could be used to estimate daily instantaneous T_a at unprecedented accuracy and temporal scale with proper training and very dense station data. The estimated dataset could be very useful for local climate and ecology studies, as well as for nature resources exploration.

Urbanization may exert a strong influence on both near-surface and atmospheric moisture. However, studies on the effect of urbanization on atmospheric moisture using remotely sensed observations have been infrequently conducted. To fill this research gap, we used remotely sensed and station-based observations to calculate urban–rural differences in total-column, near-surface, and atmospheric moisture in the Beijing metropolis. Multiple humidity indicators were selected, including precipitation water vapor, relative humidity, water vapor pressure, and absolute humidity. Results showed that both the total-column and near-surface urban dry islands (UDIs) were detected in Beijing. A significant decreasing trend of the near-surface UDIs intensity (i.e., urban minus rural) was found, which was closely related to the expansion of built-up areas (i.e., the moisture reduction in the newly urbanized areas). However, the trend of the total-column UDIs intensity was not significant. A further investigation of the atmospheric urban–rural moisture differences showed that the UDIs effect mainly existed in the near-surface layer, while an urban wet island was found above the 950 hPa pressure level, probably due to enhanced convergence in urban areas. Generally, the UDIs intensity was the strongest in the hot, wet summer and the weakest in the dry, cold winter. However, the UDIs intensity represented by relative humidity was the largest in autumn and showed the strongest correlation with the expansion of built-up areas in Beijing. This study employed satellite observations to understand the UDIs effect and highlighted the significance of urbanization-induced moisture changes on and above the ground. Findings of this study provided new insights into how urbanization affected atmospheric moisture in the boundary layer and paved the way for process-based modeling studies.

Gradually increasing durations of high temperature caused by climate change harm the health of individuals and then lead to death. This study aimed to investigate the relationship between durations of different daily mean air-temperature categories and mortality in China and forecast future mortality changes in China for 2020–2050 under Representative Concentration Pathways (RCP)4.5 and RCP8.5 scenarios. The daily mean air temperature was divided into 10 categories, and the days under each air-temperature category were counted during the period of 2000–2015. Then, the connection between the days of each of the 10 air-temperature categories and mortality was established using the semi parametric regression model. Results indicate that the days of the >32 °C category have the largest impact on mortality in China, with the death rate increasing by 23‰ for one additional day. Predictions reveal that mortality in China will increase 25.48% and 26.26% under the RCP4.5 and RCP8.5 scenarios, respectively. Moreover, the mortality of 86 regions in western China will increase 30.42%. Therefore, in the future, the increasing duration of days of high temperatures will raise the mortality rate in China and aggravate the mortality gap between developed and underdeveloped regions.

China has a marked continental monsoon climate characterized by dry and wet hazards that have destructive impacts on grape yields and quality. The purpose of this study was to analyze the spatiotemporal characteristics of dryness/wetness in the wine regions of China and explore the links between these variations and large-scale climatic factors. The crop-specific standardized precipitation evapotranspiration index (SPEI) was used to characterize the dryness/wetness using meteorological data collected at 168 meteorological stations located in or near the wine regions from 1981–2015. Results showed that most wine regions of China experienced a wetting trend. The drought and wet event characteristics were region- and site-specific. The main wine regions of China (e.g., Xinjiang, Helan Mountain and Hexi Corridor) were characterized by relatively high drought severity; the extreme drought frequencies of the three regions were higher as well (11.5%, 3.3%, and 3.6%, respectively). Xinjiang was also characterized by a high wetness severity and an extremely high wetness frequency of 16%, but the wetness severity decreased over time. A 4–6-year periodical oscillation was commonly detected over the wine regions. The dryness/wetness characteristics were highly associated with the Southern Oscillation Index, Niño 3.4 and the Indian Ocean Dipole, with highest correlation coefficients of −0.40, 0.36 and 0.43 at lag times of 11, 8, and 11 months, respectively. The serious dry and wet events that occurred in 2001 and 1998, respectively, were speculated to be associated with anomalous atmospheric circulation patterns. These results can be used to inform grapevine stakeholders at various levels (e.g., farmer and industry) for developing strategies to mitigate and adapt dryness/wetness events in the wine regions of China. It is expected that the approach proposed in this study can also be applicable to wine regions of other countries.