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Vegetation greening drives long-term dust mitigation in Eastern Asia

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Abstract

Dryland vegetation dynamics play a fundamental role in dust mitigation. While weaker winds have driven recent dust decline in Eastern Asia, the long-term influence of vegetation remains overlooked. Here, using a physically based modeling approach that partitions the drivers of dust emissions across timescales, we reveal that surface wind dominates interannual variability, negatively correlated with the El Niño–Southern Oscillation, Arctic Oscillation, and Pacific Decadal Oscillation. On a multidecadal scale, however, vegetation cover emerges as the key driver, reducing dust emissions by 32.5% since the early 2000s—a trend projected to continue through 2100. Without these vegetation gains, dust emissions would increase under various CMIP6 projections. Vegetation contributions are uneven: greening of perennial dryland vegetation in sparsely vegetated regions (<15% cover; responsible for 95% of dust emissions) offers the greatest mitigation by stabilizing the land surface and suppressing long-term emissions. These findings highlight priority areas for ecological restoration to sustain dust reduction and advance regional sustainability goals.

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Data availability

The data generated in this study have been deposited in a public repository and are available via figshare at https://doi.org/10.6084/m9.figshare.29652356.v3 (ref. 102.), with a backup copy hosted at https://doi.org/10.11888/Terre.tpdc.301534. The data used in this study are publicly available from third-party sources, including MERRA-2 data: https://disc.sci.gsfc.nasa.gov/datasets/; ERA5-Land monthly data: https://cds.climate.copernicus.eu/datasets/reanalysis-era5-land-monthly-means?tab=overview; ERA5 hourly data: https://cds.climate.copernicus.eu/datasets/reanalysis-era5-single-levels?tab=overview; Dust storm records: https://www.ncei.noaa.gov/pub/data/noaa/ and https://doi.org/10.57760/sciencedb.03301 CMIP6 data: https://aims2.llnl.gov/search/cmip6/; NEX-GDDP-CMIP6 downscaled data: https://nex-gddp-cmip6.s3.us-west-2.amazonaws.com/index.html#NEX-GDDP-CMIP6/; Population data: https://hub.worldpop.org/; Human footprint data: https://www.x-mol.com/groups/li_xuecao/news/48145; Nighttime light data: https://eogdata.mines.edu/products/vnl/; ESA-CCI land cover: https://maps.elie.ucl.ac.be/CCI/viewer/; FAO livestock data: https://www.fao.org/livestock-systems/global-distributions/en/; Climate indices: https://www.cpc.ncep.noaa.gov/ and https://www.ncei.noaa.gov/access/monitoring/products/. Additional publicly available climate and vegetation datasets are listed with detailed access links and descriptions in Supplementary Table 5.

Code availability

All data analyses were performed using MATLAB or R. The code for the dust emission model is available in the Supplementary Software provided by Wu et al.23, and can be accessed via https://doi.org/10.1038/s41467-022-34823-3. Other codes required to reproduce the results and figures presented in the main text have been deposited at https://doi.org/10.6084/m9.figshare.29652356.v3 (ref. 102).

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Acknowledgements

This study was supported by the National Natural Science Foundation of China (U2243204, 42271003) and the State Scholarship Fund (202310550002) provided by the China Scholarship Council. C. Wu is supported by the National Large Scientific and Technological Infrastructure “Earth System Numerical Simulation Facility” (https://cstr.cn/31134.02.EL).

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Authors and Affiliations

  1. College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, Gansu, China

    Yang Fu & Zhuolun Li

  2. State Key Laboratory of Tibetan Plateau Earth System, Environment and Resources (TPESER), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China

    Yang Fu, Shan Gao, Jingtian Zhang, Dou Li, Xiangyu Zheng, Yafeng Wang, Eryuan Liang & Shilong Piao

  3. CREAF, Cerdanyola del Valles, Barcelona, Catalonia, Spain

    Yang Fu, Josep Peñuelas & Jingtian Zhang

  4. CSIC, Global Ecology Unit CREAF-CSIC-UAB, Bellaterra, Barcelona, Catalonia, Spain

    Yang Fu, Josep Peñuelas & Jingtian Zhang

  5. State Key Laboratory of Earth System Numerical Modeling and Application, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

    Chenglai Wu

  6. International Center for Climate and Environment Sciences, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

    Chenglai Wu

  7. Instituto Pirenaico de Ecología (IPE-CSIC), Zaragoza, Spain

    J. Julio Camarero

  8. College of Ecology, Lanzhou University, Lanzhou, Gansu, China

    Dou Li

  9. University of Chinese Academy of Sciences, Beijing, China

    Xiangyu Zheng

  10. Institute of Carbon Neutrality, Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, China

    Shilong Piao

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Contributions

E.Y.L., Y.F.W., and S.L.P. led the concept development and writing. Y.F., C.L.W., and J.T.Z. conceived and designed the model and experiments. Y.F. performed the initial simulation, data curation, data analysis, visualization, and drafted the manuscript. C.L.W. provided the field data used to evaluate the model. S.G., J.P., and J.J.C. contributed to the concept and writing. D.L., X.Y.Z., and Z.L.L. contributed to writing and discussion. All authors reviewed and approved the manuscript.

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Yafeng Wang.

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Nature Communications thanks Caroline Pierre, who co-reviewed with Paul-Alain Raynal, and Ali Darvishi Boloorani for their contribution to the peer review of this work. A peer review file is available.

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Fu, Y., Wu, C., Gao, S. et al. Vegetation greening drives long-term dust mitigation in Eastern Asia.
Nat Commun (2026). https://doi.org/10.1038/s41467-026-68427-y

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