Based on the homogenized daily data of 2255 meteorological stations during the past 60 years from 1961 to 2020, the potential evapotranspiration was calculated using the revised FAO56 Penman–Monteith model, and then the annual AI (aridity index, the ratio of annual potential evapotranspiration to annual precipitation) was employed to analyze the dry-wet climate change in China. The GCM models’ prediction data was used to analyze the possible trends of dry-wet climate in China by the end of this century. The results showed that in the past 60 years, the climate in China was getting wetter, especially in the western regions of China, including Xinjiang, western Qinghai, Gansu, western Inner Mongolia, and northwestern Tibet. In the last 10 years, China’s climate has become more humid. Compared with the 1960s, the total area of aridity has decreased by about 650,000 square kilometers. The changes of different climate zones have regional and periodical characteristics. There was a tendency to get wet periods in all four seasons, especially in summer. Analysis of GCM model projection data shows that by the end of this century, the climate in China would have a general trend of becoming drier. The drier regions are mainly located in the central and eastern parts of China, while the western regions of China continue to maintain the wetting trends. In the case of high emissions, the trends of drying in the central and eastern and wetting in the west are more significant than in the case of medium emission.

This study aimed to improve understanding of the differences in surface air temperature data between observations and reanalysis since the beginning of the 20th century and to address the reanalysis data error. The anomaly correlation, standard deviation, and linear trend of temperature during 1909–2010 in eastern China was analyzed based on homogenized observation data from 16 stations and two sets of 20th century monthly mean surface air temperature reanalysis data (20CR and ERA20C). The results show that the inter-annual and decadal variability were consistent between reanalysis and observations in eastern China after 1979. The reanalysis data exhibited a large fluctuation during the 1960s. The average 20CR temperature was lower than the observations during 1920–1950. The inter-annual and decadal variability for winter and spring were consistent with the observations. The correlation and standard deviation ratio between the reanalysis and observations demonstrated a high consistency of their inter-annual variability and dispersion. The ERA20C data were generally closer to the observations than the 20CR data for the period 1979–2010. The linear trends of surface air temperature showed clear warming in both reanalysis datasets and the observations, but the reanalysis trends were significantly smaller than the observational trends for annual mean temperature and most of the seasonal mean temperatures after the 1950s. Overall, ERA20C was generally closer to the observational temperatures than 20CR during 1909–2010, but this consistency does not necessarily indicate ERA20C’s suitability for climate change research because of the systematic bias referenced to the observational data.

Much attention has been given to the change rule of a single extreme event, and there are few reports on comprehensive characteristics of multiple extreme events in a certain region. Based on the analyzes of annual frequency of extreme high temperature, extreme low temperature, extreme drought, extreme precipitation, and extreme typhoon events in China from 1961 to 2020, a multi extreme events composite risk grade index (MXCI) was constructed and applied to the comprehensive characteristics analyzes of multiple extreme events in China. The results show that the high value areas of MXCI were mainly located in southeast China and southwest China. Over the past 60 years, the MXCI has shown a decreasing trend in western China and most of southeastern China, and an increasing trend in the middle zone from southwest China to northeast China. Through comparative analysis, MXCI can objectively reflect the comprehensive characteristics of multiple extreme climate events in a region, which is helpful to understand regional extreme climate characteristics and effectively cope with extreme climate risks.