Previous research has extensively explored the “stilling” and “reversal” phenomena in annual near‐surface wind speed (NSWS). However, the variations in the strengths of these phenomena between different months remain unclear. Here the monthly changes in observed NSWS from 769 stations across China during 1979–2020 were analyzed. The analysis reveals a consistent decline in NSWS that ceased around 2011, followed by an increasing trend detected in all months except March, where a distinct hiatus is observed. The hiatus in March NSWS is primarily attributed to a significant reduction in NSWS over North and Northwest China. This reduction can be linked to the southward shift of the East Asian subtropical jet (EASJ), which resulted in a decreased meridional temperature gradient and weakened transient eddy activities across northern China. These findings emphasize the importance of considering changes in the EASJ to gain a comprehensive understanding of NSWS changes at regional scale.

The continuous reduction in near-surface wind speed (NSWS) before 2010, commonly referred to as ‘stilling’, has been widely observed across land areas in the Northern Hemisphere (NH), though its underlying mechanisms remain unclear. In this study, we presented robust evidence linking the Atlantic multidecadal oscillation (AMO) to the stilling over China between 1970 and 2010. Positive AMO phases trigger warming across the NH high-latitudes, weakening the large-scale meridional temperature gradient, and eventually leading to the reduction in NSWS in mid-latitude regions. We confirmed this effect through the Coupled Model Intercomparison Project phase 6 Decadal Climate Prediction Project and historical simulations, which consistently reproduce the AMO’s impact on NSWS change over China. Additionally, large-ensemble simulations suggest that excluding the contribution of AMO reduces the uncertainty in NSWS trends from 1976 to 2005 by 19%. Our findings underscore the critical footprint of natural internal variability, particularly the AMO, in modulating regional NSWS and highlights its important role in the broader climate system.

In recent decades, Southwest China (SWC) has experienced a series of severe and extensive droughts resulting in tremendous socioeconomic losses. The annual maximum dry spell length (AMDSL), which refers to the number of consecutive days without rainfall, or days with rainfall below a threshold, plays an important role in triggering drought. The main objective of this study is to provide a comparison of the capabilities of current regional climate models (RCMs) in simulating extreme dry spell characteristics in mountainous SWC. Five available RCM simulations utilized in the Coordinated Regional Climate Downscaling Experiment (CORDEX)-East Asia project over 1981–2005 were employed in this study; the RCMs being NIMR-HadGEM3, SNU-MM5, SNU-WRF, KNU-RegCM4, and YSU-RSM. First, it was found that all of the RCMs reasonably simulate the main seasonality features of rainfall and dry days in SWC. Furthermore, four of the RCMs, excepting YSU-RSM, can accurately capture the spatial pattern of dry-day occurrence based on Taylor diagram diagnosis. Second, we assessed the performance of the five RCMs to detect and reproduce the climatology and variability of the AMDSL. In general, the RCMs simulate the spatial pattern of long-term mean and interannual variability of the AMDSL in SWC well. Based on Taylor diagram evaluation, NIMR-HadGEM3 was the best among the five in simulating the AMDSL characteristics. Third, the generalized extreme value (GEV) distribution is considered the most suitable model for fitting the AMDSL in both observation and RCM experimental data in comparison to other three-parameter probabilistic models. Higher value centers of the scale parameter and the location parameter indicate a wider and amplified distribution of the AMDSL over the low-latitude highlands (LLH) region against other areas in SWC, which is consistent with the spatial patterns of climatological AMDSL. In addition, sensitivity analysis of different thresholds for dry days shows that the 1 mm threshold is suitable for this study and that different threshold choices have little effect on simulation ability. Overall, the results show that although significant differences are found between RCMs, the RCMs excepting YSU-RSM can reasonably reveal extreme dry spell occurrence and amplitude, along with the spatial distribution of the AMDSL at a 20-year return period in SWC. This information is useful for model evaluation and improvement, future climate projections, and water resource risk management.

As near‐surface wind speed (NSWS) largely controls wind power generation, robust projection is vital to wind energy planning and broader sustainability goals. However, the predictive skill of climate models for NSWS remains uncertain. Analysis of Coupled Model Intercomparison Project Phase 6 simulations shows that the models reproduce the mean NSWS over China reasonably well but substantially underestimate the observed long‐term trend and variability. Given these large biases, bias correction is essential for obtaining more reliable NSWS projections. We correct this bias using an Empirical Orthogonal Function approach to isolate the dominant spatial modes of NSWS variability. The corrected projections exhibit amplified future trends and increased variability of NSWS over China compared with the original model output, with the strongest changes emerging under higher greenhouse gas emissions. Our results indicated that unadjusted models may understate the magnitude of future NSWS changes. This study provides a more reliable reference for future evolution of NSWS.

Accurate simulation and projection of near-surface wind speed (NSWS) are vital for advancing wind energy development and addressing climate change. Despite extensive use, global climate models continue to face challenges in capturing long-term NSWS trends over land. This study uses data from the High-Resolution Model Intercomparison Project (HighResMIP) under CMIP6, to assess whether the increased horizontal resolution and ocean–atmosphere coupling could improve NSWS simulations. Contrary to expectations, our results reveal that neither higher resolution nor ocean–atmosphere coupling substantially enhances the model’s performance in replicating NSWS. This suggests that other factors, such as dynamical processes and physical parameterizations, play more critical roles in determining model performance. The findings suggest that future research should focus on employing more comprehensive datasets and diverse model configurations to effectively identify and address sources of bias in NSWS modeling.

Near‐surface wind speed (NSWS) regulates the intensity of land‐atmosphere fluxes and the viability of wind energy systems, playing a pivotal role in human adaptation to climate change. However, at the current stage, global climate models exhibit a wide inter‐model spread in NSWS simulations, limiting the reliability of future wind energy projections. Here, we identify the primary drivers of this uncertainty over China. We show that the spread is concentrated in North and Northeast China. Models simulating extensive forest loss exhibit weak or negligible NSWS trends because deforestation smooths the land surface and counteracts the background surface stilling. In contrast, models with limited forest loss or forest gain yield pronounced NSWS declines. This substantial spread is mainly attributable to the varying magnitude of forest cover changes, accounting for approximately 74% of the inter‐model spread. Our findings suggest that constraining forest cover changes is critical for substantially reducing the uncertainty in surface wind projections.