Abstract
In this study, the variations in near-surface wind speed (SWS) across China were analysed using 55 years of observational data from 2044 meteorological stations spanning 1970–2024. The results indicate that the SWS in China experienced a persistent decline from 1970 to 2004, then remained constant from 2005 to 2014 and shifted to recovery after 2015. The seasonal and spatial analyses confirmed that the slowdown period occurred nearly nationwide, whereas the apparent recovery displayed a strong spatial heterogeneity. On the basis of long-term station records and metadata from China, station-by-station analysis revealed that SWS records are strongly influenced by widespread relocations; 1237 stations (60.5% of the total stations) with SWS discontinuities were related to relocations, leading to an overestimation of the so-called “recovery phase” at national and regional scales. This emphasized the importance of homogenization to ensure the reliability of the SWS dataset. Afterwards, the SWS breakpoints were detected and adjusted. After homogenization, the SWS displayed slight but significantly negative trends (–0.06 m s⁻¹ per decade) after 2005. Representative non-relocated stations were used to validate the homogenized results, and revealed that the homogenized series accurately captured more robust signal from non-relocated stations, which confirmed the consistent declining trends across China and further implied the effect of station relocation. Notably, compared with the original records, the reduction in the homogenized SWS ranged from 3% to 11% among the subregions. This could substantially impact on the assessment of wind energy resources and requires careful attention.
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Data availability
Due to confidentiality agreements, the SWS data used in this study are not publicly available. The researchers could obtain the raw SWS and homogenized SWS data from the first author or corresponding author upon request. Additionally, the interpolated annual mean gridded SWS data over North China, generated using a method consistent with the widely adopted CN05.1 dataset, are available at https://doi.org/10.5281/zenodo.17402946.
References
Pryor, S. C. & Barthelmie, R. J. Climate change impacts on wind energy: a review. Renew. Sust. Energy Rev. 14, 430–437 (2010).
Pryor, S. C. & Barthelmie, R. J. Assessing climate change impacts on the near-term stability of the wind energy resource over the United States. Proc. Natl. Acad. Sci. USA 108, 8167–8171 (2011).
Leung, D. Y. C. & Yang, Y. Wind energy development and its environmental impact: a review. Renew. Sust. Energy Rev. 16, 1031–1039 (2012).
IRENA. Renewable energy statistics 2025 (International Renewable Energy Agency, 2025).
Jacobson, M. Z. & Kaufman, Y. Wind reduction by aerosol particles. Geophys. Res. Lett. 33, L24814 (2006).
Lu, X., McElroy, M. B. & Kiviluoma, J. Global potential for wind-generated electricity. Proc. Natl. Acad. Sci. USA 106, 10933–10938 (2009).
Tobin, I. et al. Land surface wind speed (in “State of the Climate in 2013”). Bull. Am. Meteorol. Soc. 95, S28–S29 (2014).
Lin, C. G. et al. Impacts of wind stilling on solar radiation variability in China. Sci. Rep. 5, 15135. https://doi.org/10.1038/srep15135 (2015).
Zhang, N. et al. Numerical simulations on influence of urban land cover expansion and anthropogenic heat release on urban meteorological environment in Pearl River Delta. Theor. Appl. Climatol. 126, 469–479 (2016).
Wang, X. et al. PM2.5 pollution in China and how it has been exacerbated by terrain and meteorological conditions. Bull. Am. Meteorol. Soc. 99, 105–119 (2018).
Zhang, Y. et al. Decreasing atmospheric visibility associated with weakening winds from 1980 to 2017 over China. Atmos. Environ. 222, 117314 (2020).
Paulot, F., Naik, V. & Horowitz, L. Reduction in near-surface wind speeds with increasing CO2 may worsen winter air quality in the Indo-Gangetic plain. Geophys. Res. Lett. 49, e2022GL099039. https://doi.org/10.1029/2022GL099039 (2022).
Hobbins, M. T. Regional evapotranspiration and pan evaporation: complementary interactions and long-term trends across the conterminous United States. Ph.D. thesis, Colorado State Univ., Fort Collins (2004).
Rayner, D. P. Wind run changes: the dominant factor affecting pan evaporation trends in Australia. J. Clim. 20, 3379–3394 (2007).
Song, Z. W. et al. Distribution and trends in reference evapotranspiration in the North China Plain. J. Irrig. Drain. Eng. 136, 240–247 (2010).
McVicar, T. R. et al. Global review and synthesis of trends in observed terrestrial near-surface wind speeds: implications for evaporation. J. Hydrol. 416, 182–205 (2012).
Wang, K. & Dickinson, R. E. A review of global terrestrial evapotranspiration: observation, modeling, climatology, and climatic variability. Rev. Geophys. 50, RG2005 (2012).
Liu, X. et al. Effect of surface wind speed decline on modeled hydrological conditions in China. Hydrol. Earth Syst. Sci. 18, 2803–2813 (2014).
Li, Y. P. et al. Effects of land use and cover change on surface wind speed in China. J. Arid Land 11, 345–356 (2019).
Zheng, D. S. et al. Climate change impacts on the extreme power shortage events of wind-solar supply systems worldwide during 1980–2022. Nat. Commun. 15, 5225 (2024).
Zheng, D. S. et al. Strategies for climate-resilient global wind and solar power systems. Nature 643, 1263–1270 (2025).
Roderick, M. L. et al. On the attribution of changing pan evaporation. Geophys. Res. Lett. 34, L17403 (2007).
Pryor, S. C. et al. Wind speed trends over the contiguous US. J. Geophys. Res. D. 114, D14105 (2009).
Dunn, R. J. H. et al. HadISD: A quality-controlled global synoptic report database for selected variables at long-term stations from 1973–2011. Climate 8, 1649–1679 (2012).
Li, D., von Storch, H. & Geyer, B. High-resolution wind hindcast over the Bohai Sea and the Yellow Sea in East Asia: evaluation and wind climatology analysis. J. Geophys. Res. Atmos. 121, 111–129 (2016).
Zeng, Z. et al. A reversal in global terrestrial stilling and its implications for wind energy production. Nat. Clim. Change 9, 979–985 (2019).
Ding, Y. H., Li, X. & Li, Q. P. Advances of surface wind speed changes over China under global warming. J. Appl. Meteorol. Sci. 31, 1–12 (2020).
Dumitrescu, A. et al. Recent climatic changes in Romania from observational data (1961–2013). Theor. Appl. Climatol. 122, 111–119 (2014).
Vautard, R. et al. Northern Hemisphere atmospheric stilling partly attributed to an increase in surface roughness. Nat. Geosci. 3, 756–761 (2010).
Wu, J. et al. Changes in terrestrial near-surface wind speed and their possible causes: an overview. Clim. Dyn. 51, 2039–2078 (2018).
Zhang, G. et al. Rapid urbanization induced daily maximum wind speed decline in metropolitan areas: a case study in the Yangtze River Delta (China). Urban Clim. 43, 101147 (2022).
Berrisford, P., Tobin, I., Dunn, R. J. H., Vautard, R. & McVicar, T. R. Land surface wind speed (in “State of the Climate in 2014. Bull. Am. Meteorol. Soc. 95, S33–S34 (2015).
Dunn, R. J. H. et al. Surface winds (in “State of the climate in 2015”). Bull. Am. Meteorol. Soc. 97, S38–S40 (2016).
Azorin-Molina, C., Dunn, R. J. H., Mears, C. A., Berrisford, P. & McVicar, T. R. Surface winds [in “State of the climate in 2016”]. Bull. Am. Meteorol. Soc. 98, S37–S39 (2017).
Wohland, J., Folini, D. & Pickering, B. Wind speed stilling and its recovery due to internal climate variability. Earth Syst. Dyn. 12, 1239–1251 (2021).
Zhou, L. H. et al. A continuous decline of global seasonal wind speed range over land since 1980. J. Clim. 9443–9461; (2021).
Wu, J. & Shi, Y. Changes in surface wind speed and its different grades over China during 1961–2020 based on a high-resolution dataset. Int. J. Climatol. https://doi.org/10.1002/joc.7453 (2021).
Miao, H. Z. Y. et al. The reversal of surface wind speed trend in Northeast China: impact from aerosol emissions. Clim. Dyn. 63, 43 (2025).
Guo, H., Xu, M. & Hu, Q. Changes in near-surface wind speed in China: 1969–2005. Int. J. Climatol. 31, 349–358 (2011).
Kim, J. C. & Paik, K. Recent recovery of surface wind speed after decadal decrease: a focus on South Korea. Clim. Dyn. 45, 1699–1712 (2015).
Yang, X. et al. The decreasing wind speed in southwestern China during 1969–2009, and possible causes. Quat. Int. 263, 71–84 (2012).
Zha, J. L., Wu, J., Zhao, D. M. & Tang, J. P. A possible recovery of the near-surface wind speed in Eastern China during winter after 2000 and the potential causes. Theor. Appl. Climatol. 136, 119–134 (2019).
Zhang, Z. T. & Wang, K. Stilling and recovery of the surface wind speed based on observation, reanalysis, and geostrophic wind theory over China from 1960 to 2017. J. Clim. 33, 3989–4008 (2020).
Wu, J., Shi, Y. & Xu, Y. Evaluation and projection of surface wind speed over China based on CMIP6 GCMs. J. Geophys. Res. Atmos. 125, e2020JD033611 (2020).
Enloe, J., O’Brien, J. J. & Smith, S. ENSO impacts on peak wind gusts in the United States. J. Clim. 17, 1728–1737 (2004).
Xu, M. et al. Steady decline of East Asian monsoon winds, 1969–2000: evidence from direct ground measurements of wind speed. J. Geophys. Res. Atmos. 111, D24111 (2006).
Fu, G. B. et al. Temporal variation of wind speed in China for 1961–2007. Theor. Appl. Climatol. 104, 313–324 (2011).
Clifton, A. & Lundquist, J. K. Data clustering reveals climate impacts on local wind phenomena. J. Appl. Meteorol. Climatol. 51, 1547–1557 (2012).
Earl, N. et al. 1980–2010 variability in U.K. surface wind climate. J. Clim. 26, 1172–1191 (2013).
Chen, L., Li, D. & Pryor, S. C. Wind speed trends over China: quantifying the magnitude and assessing causality. Int. J. Climatol. 33, 2579–2590 (2013).
Wever, N. Quantifying trends in surface roughness and the effect on surface wind speed observations. J. Geophys. Res. 117, D11 (2012).
Zha, J. L., Shen, C. & Zhao, D. M. Slowdown and reversal of terrestrial near-surface wind speed and its future changes over eastern China. Environ. Res. 16, 034028 (2021).
Li, D. et al. Statistical bias correction for simulated wind speeds over CORDEX-East Asia. Earth Space Sci. 6, 200–211 (2019).
Stull, R. B. An introduction to boundary layer meteorology. https://doi.org/10.1007/978-94-009-3027-8 (Springer, 1988).
Ramanathan, V. C. et al. Atmospheric brown clouds: Impacts on South Asian climate and hydrological cycle. Proc. Natl. Acad. Sci. USA 102, 5326–5333 (2005).
Bichet, A., Wild, M., Folini, D. & Schär, C. Causes for decadal variations of speed over land: sensitivity studies with a global climate model. Geophys. Res. Lett. 39, L11701 (2012).
Li, Y., Chen, Y., Li, Z. & Fang, G. Recent recovery of surface wind speed in northwest China. Int. J. Climatol. 38, 4445–4458 (2018).
Wan, H., Wang, X. L. & Swail, V. R. Homogenization and trend analysis of Canadian near-surface wind speeds. J. Clim. 23, 1209–1225 (2010).
Degaetano, A. T. Identification and implications of biases in U.S. surface wind observation, archival and summarization methods. Theor. Appl. Climatol. 60, 151–162 (1998).
Feng, S., Hu, Q. & Qian, W. H. Quality control of daily meteorological data in China, 1951–2000: a new dataset. Int. J. Climatol. 24, 853–870 (2004).
Masters, F. J. et al. Toward objective, standardized intensity estimates from surface wind speed observations. Bull. Am. Meteorol. Soc. 91, 1665–1682 (2010).
Thomas, B. R. & Swail, V. R. Buoy wind inhomogeneities related to averaging method and anemometer type: application to long time series. Int. J. Climatol. 31, 1040–1055 (2011).
Hong, H. P. et al. Basis for recommending an update of wind velocity pressures in Canadian design codes. Can. J. Civ. Eng. 41, 206–221 (2014).
Azorin-Molina, C. et al. Evaluating anemometer drift: A statistical approach to correct biases in wind speed measurement. Atmos. Res. 203, 175–188 (2018).
Dunn, R. J. H. et al. Reduction in reversal of global stilling arising from correction to encoding of calm periods. Environ. Res. Commun. 4, 061003 (2022).
Patzert, W., LaDochy, S. D., Ramirez, P. & Willis, J. Los Angeles Weather Station’s relocation impacts climatic and weather records. Calif. Geogr. 55, 42–52 (2016).
Liu, Y. et al. Impacts of anemometer changes, site relocations and processing methods on wind speed trends in China. Atmos. Meas. Tech. 17, 1123–1131 (2024).
IPCC. Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (Cambridge Univ. Press, 2021); https://doi.org/10.1017/9781009157896 (2021).
Ramon, J., Lledó, L., Torralba, V., Soret, A. & Doblas-Reyes, F. J. What global reanalysis best represents near-surface winds? Q. J. R. Meteorol. Soc. 145, 3236–3251 (2019).
Molina, M. O., Gutiérrez, C. & Sánchez, E. Comparison of ERA5 surface wind speed climatologies over Europe with observations from the HadISD dataset. Int. J. Climatol. 41, 4864–4878 (2021).
Gandoin, R. & Garza, J. Underestimation of strong wind speeds offshore in ERA5: evidence, discussion and correction. Wind Energy Sci. 9, 1727–1745 (2024).
Liu, W. L. et al. Rapid acceleration of Arctic near-surface wind speed in a warming climate. Geophys. Res. Lett. 51, e2024GL109385 (2024).
Shen, C. et al. Increases of offshore wind potential in a warming world. Geophys. Res. Lett. 51, e2024GL109494 (2024).
Fan, W. et al. Evaluation of global reanalysis land surface wind speed trends to support wind energy development using in situ observations. J. Appl. Meteorol. Climatol. 60, 33–50 (2021).
Shen, C. et al. Does CRA-40 outperform other reanalysis products in evaluating near-surface wind speed changes over China?. Atmos. Res. 266, 105948 (2021).
Wang, X. L. et al. Trends and variability of storminess in the Northeast Atlantic region, 1874–2007. Clim. Dyn. 33, 1179–1195 (2008).
Shen, C. et al. March near-surface wind speed hiatus over China since 2011. Geophys. Res. Lett. 50, e2023GL104230 (2023).
Zhang, Z. T. & Wang, K. Homogenization of observed surface wind speed based on geostrophic wind theory over China from 1970 to 2017. J. Clim. 36, 3667–3679 (2023).
Wang, X. L. & Feng, Y. RHtestsV4 user manual. Climate Research Division, Atmospheric Science and Technology Directorate, Science and Technology Branch, Environment Canada, 29 https://etccdi.pacificclimate.org/RHtest/RHtestsV4_UserManual_10Dec2014.pdf (2013).
Lee, J. C. Y. & Fields, M. J. An overview of wind-energy-production prediction bias, losses and uncertainties. Wind Energy Sci. 6, 311–365; (2021).
Shi, T., Yang, Y. J., Sun, D. B., Huang, Y. & Shi, C. Influence of changes in meteorological observational environment on urbanization bias in surface air temperature: a review. Front. Clim. 3, 781999 (2022).
Zhou, C. L. et al. A century-long homogenized dataset of near-surface wind speed observations since 1925 rescued in Sweden. Earth Syst. Sci. Data. 14, 2167–2177 (2022).
Cao, L. J., and Yan Z. W. Progress in research on homogenization of climate data. Adv. Clim. Change Res. 3, 59-67 (2012).
Andres-Martin, M. et al. Uncertainty in surface wind speed projections over the Iberian Peninsula: CMIP6 GCMs versus a WRF-RCM. Ann. NY Acad. Sci. 1529, 101–108 (2023).
Deng, H., Hua, W. & Fan, G. Z. Evaluation and projection of near-surface wind speed over China based on CMIP6 models. Atmospheric 12, 1062 (2020).
Zha, J. L., Zhao, D. M. & Fan, W. X. Future projections of the near-surface wind speed over eastern China based on CMIP5 datasets. Clim. Dyn. 54, 2361–2385 (2020).
Shen, C. et al. Centennial-scale variability of terrestrial near-surface wind speed over China from reanalysis. J. Clim. 34, 5829–5846 (2021).
Li, Z.-B., Sun, M., Shen, C. & Chen, D. L. ENSO-driven seasonal variability in near-surface wind speed and wind power potential across China. Geophys. Res. Lett. 52, e2025GL115537 (2025).
Shen, C., Li, Z. B., Liu, F., Chen, H. W. & Chen, D. L. A robust reduction in near-surface wind speed after volcanic eruptions: Implications for wind energy generation. Innovation 6, 100734 (2025).
Shen, C. et al. Unravelling the future role of internal variability in South Asian near-surface wind speed. Earth Syst. Dyn. 16, 1959–1979 (2025).
Miao, H., Xu, H., Huang, G. & Yang, K. Evaluation and future projections of wind energy resources over the Northern Hemisphere in CMIP5 and CMIP6 models. Renew. Energy 211, 809–821 (2023).
WMO. WMO guidelines on the calculation of climate normals, WMO-No. 1203. https://library.wmo.int/viewer/55797?medianame=1203_en_#page=1&viewer=picture&o=bookmark&n=0&q (2017).
Cao, L. J., Ju, X. H. & Liu, X. N. Penalized maximal F test for the homogeneity study of the annual mean wind speed over China. Meteorol. Mon. 36, 52–56 (2010).
Azorin-Molina, C. et al. An approach to homogenize daily peak wind gusts: An application to the Australian series. Int. J. Climatol. 39, 2260–2277 (2019).
Wang, X. L., Chen, H. F., Wu, Y. H., Feng, Y. & Pu, Q. New techniques for the detection and adjustment of shifts in daily precipitation data series. J. Appl. Meteor. Climatol. 49, 2416–2436 (2010).
Sen, P. K. Estimates of the regression coefficient based on Kendall’s tau. J. Am. Stat. Assoc. 63, 1379–1389 (1968).
Theil, H. A rank-invariant method of linear and polynomial regression analysis. Henri Theil's. Contributions Econ. Econ. (Adv. Stud. Theor). Appl. Econ. 23, 20 (1992).
Mondal, A., Kundu, S. & Mukhopadhyay, A. Rainfall trend analysis by Mann-Kendall test: a case study of north-eastern part of Cuttack district, Orissa. Int. J. Geol. Earth Environ. Sci. 2, 70–78 (2012).
You, Q. et al. Observed surface wind speed in the Tibetan Plateau since 1980 and its physical causes. Int. J. Climatol. 34, 1873–1882 (2014).
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We appreciate the meteorological measurements provided by China Meteorological Administration (http://data.cma.cn/).
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Y.Y. and W.J. conceived and designed the study, developed the methodology, conducted the investigation, performed formal analysis, and drafted the original manuscript. C.Q. contributed to the study framework design and acquired funding. S.Y. participated in result discussions and manuscript editing. All authors were involved in writing the paper, as well as in discussing and interpreting the results.
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Yan, Y., Wu, J., Chao, Q. et al. Overestimation of the recent observed near-surface wind speed recovery in China. npj Clim Atmos Sci (2026). https://doi.org/10.1038/s41612-026-01322-x
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DOI: https://doi.org/10.1038/s41612-026-01322-x
