Statistical downscaling and dynamical downscaling are two approaches to generate high‐resolution regional climate models based on the large‐scale information from either reanalysis data or global climate models. In this study, these two downscaling methods are used to simulate the surface climate of China and compared. The Statistical Downscaling Model (SDSM) is cross validated and used to downscale the regional climate of China. Then, the downscaled historical climate of 1981–2000 and future climate of 2041–2060 are compared with that from the Weather Research and Forecasting (WRF) model driven by the European Center‐Hamburg atmosphere model and the Max Planck Institute Ocean Model (ECHAM5/MPI‐OM) and the L'Institut Pierre‐Simon Laplace Coupled Model, version 5, coupled with the Nucleus for European Modelling of the ocean, low resolution (IPSL‐CM5A‐LR). The SDSM can reproduce the surface temperature characteristics of the present climate in China, whereas the WRF tends to underestimate the surface temperature over most of China. Both the SDSM and WRF require further work to improve their ability to downscale precipitation. Both statistical and dynamical downscaling methods produce future surface temperatures for 2041–2060 that are markedly different from the historical climatology. However, the changes in projected precipitation differ between the two downscaling methods. Indeed, large uncertainties remain in terms of the direction and magnitude of future precipitation changes over China.

Horizontal visibility and water vapor pressure data from 483 stations in China from 1960 to 2009 were used to inverse aerosol optical depth (AOD) using a new series of parameters, which were revised for each station using the polybasic nonlinear regression method based on monthly Multiangle Imaging Spectroradiometer (MISR) AOD data during 2001–2005. Monthly AOD retrievals from 2006 to 2009 using the new parameters were compared with MISR AOD in the same period. AOD retrieved with the new parameters can capture the extreme values better than the inversion result with the original parameters, and the root‐mean‐square error between retrieval and MISR AOD reached the minimum of 0.047 in northwest China and the maximum of 0.102 in the Huang–Huai River Basin, respectively. The spatial distribution and seasonal features of monthly AOD from 1960 to 2009 in China were also analyzed. The monthly AOD in China for past 50 years, from 1960 to 2009, exhibited an increasing trend, especially during 1960–1985, with a linear rate of increase of 0.004/year. No distinct trend can be found for AOD from 1985 to 1995; then it increased obviously from 1996 to 2009 with an annual rate of 0.002. Seasonal change in AOD showed that it reached a maximum value in summer with the average value between 0.34 and 0.36 for the whole China, while a minimum value was reached in winter with a mean value between 0.2 and 0.22. It is noted that the aerosol vertical profile was a sensitive factor to the inversion result. The root‐mean‐square error using Goddard Chemistry Aerosol Radiation and Transport aerosol profiles was increased 1.9 to 5.7 times than using the traditional three‐segment profile in northwest, southwest, northeast, and northern China, which meant that the retrieval AOD was sensitive to aerosol vertical profile in these regions; at the same time, the root‐mean‐square error was decreased 1.13 to 1.37 times in other regions.

This paper investigates the extreme wet‐bulb temperatures (TWs) in China, with a focus on understanding the relative contributions of temperature and moisture to the extremes. Analysis of station observations shows that daily extreme temperatures (T), specific humidity (q), and TWs generally co‐occur in Southeastern China, while extreme TW and T rarely overlap in the arid and semiarid North and Northwest China. Overall, q contributes more than T to extreme TWs, especially in North and Northwest China. Based on the relative contributions of q and T, regional extreme TW events are classified as q dominated and T dominated, respectively, to study their large‐scale environment. Cluster analysis of global reanalysis data shows that extreme TWs are generally accompanied by increased surface air temperature and humidity, concomitant with anomalous high pressure and notable water vapor flux convergence. However, important differences are also seen in the large‐scale environment during q‐dominated and T‐dominated extreme TWs. During q‐dominated extreme TWs, the large‐scale environment favors convection, as indicated by ascending motions, decreased downward solar radiation as well as increased precipitable water and near‐surface relative humidity. In contrast, during T‐dominated extreme TWs, convection is inhibited by the large‐scale environment that features descending motion and increased downward solar radiation, decreased precipitable water, and decreased relative humidity. Consistent with the contrasting environments, we demonstrate that T‐dominated extreme TWs tend to last longer than q‐dominated extremes. Given the significant role of moisture in extreme TWs, more research is needed to understand its impacts on heat stress now and in the future.