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drag forces are caused by changes in the external and internal friction in the atmosphere. Changes in surface friction are mainly caused by changes in the surface roughness due to land use and cover change (LUCC), including urbanization, and changes in internal friction are mainly induced by changes in the boundary layer characteristics. Future studies should compare the spatio-temporal differences in SWS between high and low altitudes and quantify the effects of different factors on the SWS. Additionally, in-depth analysis of extreme SWS events and prediction of future mean and extreme SWS values at global and regional scales are also necessary. Abstract Changes in terrestrial near-surface wind speed (SWS) are induced by a combination of anthropogenic activities and natural climate changes. Thus, the study of the long-term changes of SWS and their causes is very important for recognizing the effects of these processes. Although the slowdown in SWS has been analyzed in previous studies, to the best of knowledge, no overall comparison or detailed examination of this research has been performed. Similarly, the causes of the decreases in SWS and the best directions of future research have not been discussed in depth. Therefore, we analyzed a series of studies reporting SWS trends spanning the last 30 years from around the world. The changes in SWS differ among different regions. The most significant decreases have occurred in Central Asia and North America, with mean linear trends of − 0.11 m s −1 decade −1 ; the second most significant decreases have occurred in Europe, East Asia, and South Asia, with mean linear trends of − 0.08 m s −1 decade −1 ; and the weakest decrease has occurred in Australia. Although the SWS in Africa has decreased, this region lacks long-term observational data. Therefore, the uncertainties in the long-term SWS trend are higher in this region than in other regions. The changes in SWS, caused by a mixture of global-, regional-, and localscale factors, are mainly due to changes in driving forces and drag forces. The changes in the driving forces are caused by changes in atmospheric circulation, and the changes in the Keywords

Based on the daily maximum temperatures (Tmax) from 587 surface observation stations in China during 1959–2013, heat waves are detected using both absolute and relative definitions. The spatiotemporal variations of heat wave occurrence/duration/amplitude are compared between the two definitions. Considering the significant differences in regional climatology, relative threshold is more meaningful to detect the local extremes. By utilizing the empirical orthogonal function, the integral index heat wave total intensity is decomposed into three dominant modes: interdecadal (ID), interannual‐tripole (IA‐TR), and interannual‐dipole (IA‐DP) modes. The ID mode shows uniform anomalies over the whole China, with the maximum in north, and its corresponding time series depict notable interdecadal variations with a turning point around mid‐1990s. The IA‐DP mode exhibits opposite‐signed anomalies over north and south China. The IA‐TR mode shows an anomalous tripole pattern with negative anomalies over central China and positive anomalies over north and south China in its positive phase. Both the IA‐DP and IA‐TR patterns are more obvious since mid‐1990s with mainly year‐to‐year variations before that. All the three modes are controlled by anomalous high‐pressure systems, which are accompanied by local‐scale dry land conditions. The diabatic heating associated with anomalous convective activities over tropical western Pacific triggers Rossby wave trains propagating northward along the East Asia, which causes abnormal heat waves through descending motion over the high‐pressure nodes. In turn, the severe convections are generated by enhanced Walker circulation in the tropical Pacific due to warming and/or cooling sea surface temperature (SST) anomalies in the tropical western and eastern Pacific, respectively.

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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.

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