As one of the most principal meteorological factors to affect global climate change and human sustainable development, temperature plays an important role in biogeochemical and hydrosphere cycle. To date, there are a wide range of temperature data sources and only a detailed understanding of the reliability of these datasets can help us carry out related research. In this study, the hourly and daily near-surface air temperature observations collected at national automatic weather stations (NAWS) in China were used to compare with the China Meteorological Administration (CMA) Land Data Assimilation System (CLDAS) and the Global Land Data Assimilation System (GLDAS), both of which were developed by using the advanced multi-source data fusion technology. Results are as follows. (1) The spatial and temporal variations of the near-surface air temperature agree well between CLDAS and GLDAS over major land of China, except that spatial details in high mountainous areas were not sufficiently displayed in GLDAS; (2) The near-surface air temperature of CLDAS were more significantly correlated with observations than that of GLDAS, but more caution is necessary when using the data in mountain areas as the accuracy of the datasets gradually decreases with increasing altitude; (3) CLDAS can better illustrate the distribution of areas of daily maximum above 35 °C and help to monitor high temperature weather. The main conclusion of this study is that CLDAS near-surface air temperature has a higher reliability in China, which is very important for the study of climate change and sustainable development in East Asia.

Drought is a typical disaster in the main soybean production area of northeast China. The spatiotemporal variations of drought related to soybean production based on a crop water deficit index (CWDI) and sensitivity to meteorological variables were investigated in northeast China using daily meteorological data from 87 weather stations from 1981 to 2010. Statistical analysis revealed that precipitation could not meet the water demands of soybeans during the seedling–branching, filling, and maturing stages, and excessive drought occurred more often in northeast China. The Mann–Kendall test indicated that the soybean CWDI significantly increased during the filling stage. Kriging spatial analysis showed that the most drought-prone area was located in the west of northeast China. Explanations for the spatiotemporal variations of the drought for soybean production were explored in terms of meteorological variables. Statistical analysis showed that the crop evapotranspiration, air temperature, wind speed, and number of sunshine hours were significantly higher and the precipitation and relative humidity were significantly lower in the drought-prone area than in the dry area less prone to droughts. An explored method of sensitive analysis quantitatively revealed that precipitation and humidity negatively affected the CWDI, whereas temperature, wind speed, and number of sunshine hours positively affected the CWDI. The CWDI was most sensitive to precipitation. These results not only provide valuable information for soybean planning and management but also produce important background and physical evidence for the influence of climate on the drought related to soybean production in northeast China.