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Precipitation regime changes in High Mountain Asia driven by cleaner air

Nature volume 623, pages 544–549 (2023)Cite this article

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Abstract

High Mountain Asia (HMA) has experienced a spatial imbalance in water resources in recent decades, partly because of a dipolar pattern of precipitation changes known as South Drying–North Wetting1. These changes can be influenced by both human activities and internal climate variability2,3. Although climate projections indicate a future widespread wetting trend over HMA1,4, the timing and mechanism of the transition from a dipolar to a monopolar pattern remain unknown. Here we demonstrate that the observed dipolar precipitation change in HMA during summer is primarily driven by westerly- and monsoon-associated precipitation patterns. The weakening of the Asian westerly jet, caused by the uneven emission of anthropogenic aerosols, favoured a dipolar precipitation trend from 1951 to 2020. Moreover, the phase transition of the Interdecadal Pacific Oscillation induces an out-of-phase precipitation change between the core region of the South Asian monsoon and southeastern HMA. Under medium- or high-emission scenarios, corresponding to a global warming of 0.6–1.1 °C compared with the present, the dipolar pattern is projected to shift to a monopolar wetting trend in the 2040s. This shift in precipitation patterns is mainly attributed to the intensified jet stream resulting from reduced emissions of anthropogenic aerosols. These findings underscore the importance of considering the impact of aerosol emission reduction in future social planning by policymakers.

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Fig. 1: The two leading modes of summer precipitation variations over HMA at an interdecadal time scale.
Fig. 2: Externally forced changes in precipitation over HMA.
Fig. 3: IPO-dominated changes in South Asian summer monsoon and related precipitation patterns in and around HMA.
Fig. 4: Future changes in precipitation over HMA and the ToE.

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Data availability

The large-ensemble simulations and multimodel simulations are publicly available from the Earth System Grid Federation (https://esgf-node.llnl.gov/search/cmip6/). The GPCC data are available from the National Oceanic and Atmospheric Administration (NOAA) Physical Sciences Laboratory (https://psl.noaa.gov/data/gridded/data.gpcc.html). The CRU data are available from the CEDA Archive (https://catalogue.ceda.ac.uk/). The CN05.1 data are provided by the Climate Change Research Center, Chinese Academy of Sciences (https://ccrc.iap.ac.cn/resource/detail?id=228). The APHRODITE data are available from APHRODITE’s water resources (http://aphrodite.st.hirosaki-u.ac.jp/products.html). The GPM IMERG v.06B final run can be obtained from NASA (https://gpm.nasa.gov/data/imerg). HAR v.2 is available from Fachgebiet Klimatologie (https://www.klima.tu-berlin.de/index.php?show=daten_har2&lan=de). The JRA-55 reanalysis products can be downloaded from the NCAR Research Data Archive (https://rda.ucar.edu/datasets/ds628-1/). The ERA-5 reanalysis products are available from the Climate Data Store (https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land-monthly-means?tab=form). The ERSST data were provided by the NOAA National Centers for Environmental Information (https://www.ncei.noaa.gov/products/extended-reconstructed-sst). Source data are provided with this paper.

Code availability

The data in this study are analysed with the NCAR Command Language (NCL) and Scilab. All the maps are generated using NCL. The core codes of the optimal fingerprint method are provided by ref. 72. Other key codes are publicly available from Harvard Dataverse (https://doi.org/10.7910/DVN/VKXNNW).

References

  1. Yao, T. et al. The imbalance of the Asian water tower. Nat. Rev. Earth Environ. 3, 618–632 (2022).

    Article  ADS  Google Scholar 

  2. Zhao, D., Zhang, L. & Zhou, T. Detectable anthropogenic forcing on the long-term changes of summer precipitation over the Tibetan Plateau. Clim. Dyn. 59, 1939–1952 (2022).

    Article  Google Scholar 

  3. Ding, Z., Zhai, P. & Wu, R. Recent change in summer rainfall over the Tibetan Plateau: roles of anthropogenic forcing and internal variability. Clim. Dyn. 61, 1887–1902 (2023).

    Article  Google Scholar 

  4. Lalande, M., Ménégoz, M., Krinner, G., Naegeli, K. & Wunderle, S. Climate change in the High Mountain Asia in CMIP6. Earth Syst. Dyn. 12, 1061–1098 (2021).

    Article  ADS  Google Scholar 

  5. Farinotti, D. et al. A consensus estimate for the ice thickness distribution of all glaciers on Earth. Nat. Geosci. 12, 168–173 (2019).

    Article  ADS  CAS  Google Scholar 

  6. Immerzeel, W. W. & Bierkens, M. F. P. Asia’s water balance. Nat. Geosci. 5, 841–842 (2012).

    Article  ADS  CAS  Google Scholar 

  7. Xu, X., Lu, C., Shi, X. & Gao, S. World water tower: an atmospheric perspective. Geophys. Res. Lett. 35, L20815 (2008).

    Article  ADS  Google Scholar 

  8. A Scientific Assessment of the Third Pole Environment (United Nations Environment Programme, 2022).

  9. Yao, T. et al. Different glacier status with atmospheric circulations in Tibetan Plateau and surroundings. Nat. Clim. Change 2, 663–667 (2012).

    Article  ADS  Google Scholar 

  10. Zhang, G. et al. Extensive and drastically different alpine lake changes on Asia’s high plateaus during the past four decades. Geophys. Res. Lett. 44, 252–260 (2017).

    Article  ADS  Google Scholar 

  11. Treichler, D., Kääb, A., Salzmann, N. & Xu, C.-Y. Recent glacier and lake changes in High Mountain Asia and their relation to precipitation changes. Cryosphere 13, 2977–3005 (2019).

    Article  ADS  Google Scholar 

  12. Immerzeel, W. W., van Beek, L. P. H. & Bierkens, M. F. P. Climate change will affect the Asian water towers. Science 328, 1382–1385 (2010).

    Article  ADS  CAS  PubMed  Google Scholar 

  13. Palazzi, E., von Hardenberg, J. & Provenzale, A. Precipitation in the Hindu-Kush Karakoram Himalaya: observations and future scenarios. J. Geophys. Res. Atmos. 118, 85–100 (2013).

    Article  ADS  Google Scholar 

  14. Kuang, X. & Jiao, J. J. Review on climate change on the Tibetan Plateau during the last half century. J. Geophys. Res. Atmos. 121, 3979–4007 (2016).

    Article  ADS  Google Scholar 

  15. Gao, Y., Leung, L. R., Zhang, Y. & Cuo, L. Changes in moisture flux over the Tibetan Plateau during 1979–2011: insights from a high-resolution simulation. J. Clim. 28, 4185–4197 (2015).

    Article  ADS  Google Scholar 

  16. Yu, R.-C., Li, J., Zhang, M.-M., Li, N.-N. & Zhao, Y. South drying and north wetting over the Tibetan Plateau modulated by a zonal temperature dipole across timescales. Adv. Clim. Change Res. 14, 276–285 (2023).

    Article  Google Scholar 

  17. Tong, K., Su, F., Yang, D., Zhang, L. & Hao, Z. Tibetan Plateau precipitation as depicted by gauge observations, reanalyses and satellite retrievals. Int. J. Climatol. 34, 265–285 (2014).

    Article  Google Scholar 

  18. Wang, Z., Yang, S., Luo, H. & Li, J. Drying tendency over the southern slope of the Tibetan Plateau in recent decades: role of a CGT-like atmospheric change. Clim. Dyn. 59, 2801–2813 (2022).

    Article  Google Scholar 

  19. Yue, S. et al. Mechanisms of the decadal variability of monsoon rainfall in the southern Tibetan Plateau. Environ. Res. Lett. 16, 014011 (2021).

    Article  ADS  Google Scholar 

  20. Zhou, C., Zhao, P. & Chen, J. The interdecadal change of summer water vapor over the Tibetan Plateau and associated mechanisms. J. Clim. 32, 4103–4119 (2019).

    Article  ADS  Google Scholar 

  21. Liu, Y. et al. Anthropogenic forcing and Pacific internal variability-determined decadal increase in summer precipitation over the Asian water tower. Npj Clim. Atmos. Sci. 6, 38 (2023).

    Article  Google Scholar 

  22. Zhao, Y. & Zhou, T. Interannual variability of precipitation recycle ratio over the Tibetan Plateau. J. Geophys. Res. Atmos. 126, e2020JD033733 (2021).

    Article  ADS  Google Scholar 

  23. Sabin, T. P. et al. in Assessment of Climate Change over the Indian Region (eds Krishnan, R. et al.) 207–222 (Springer Singapore, 2020).

  24. Azam, M. F. et al. Glaciohydrology of the Himalaya-Karakoram. Science 373, eabf3668 (2021).

    Article  CAS  PubMed  Google Scholar 

  25. Wang, Y., Jiang, J. H. & Su, H. Atmospheric responses to the redistribution of anthropogenic aerosols. J. Geophys. Res. Atmos. 120, 9625–9641 (2015).

    Article  ADS  CAS  Google Scholar 

  26. Jin, Q. & Wang, C. A revival of Indian summer monsoon rainfall since 2002. Nat. Clim. Change 7, 587–594 (2017).

    Article  ADS  Google Scholar 

  27. Jiang, J. & Zhou, T. Global monsoon responses to decadal sea surface temperature variations during the twentieth century: evidence from AGCM simulations. J. Clim. 32, 7675–7695 (2019).

    Article  ADS  Google Scholar 

  28. Huang, X. et al. The recent decline and recovery of Indian summer monsoon rainfall: relative roles of external forcing and internal variability. J. Clim. 33, 5035–5060 (2020).

    Article  ADS  Google Scholar 

  29. Matsuno, T. Quasi-geostrophic motions in the equatorial area. J. Meteorol. Soc. Jpn. Ser. II 44, 25–43 (1966).

    Article  ADS  Google Scholar 

  30. Gill, A. E. Some simple solutions for heat-induced tropical circulation. Q. J. R. Meteorol. Soc. 106, 447–462 (1980).

    ADS  Google Scholar 

  31. Jiang, X. & Ting, M. A dipole pattern of summertime rainfall across the Indian subcontinent and the Tibetan Plateau. J. Clim. 30, 9607–9620 (2017).

    Article  ADS  Google Scholar 

  32. Dong, B. & Dai, A. The influence of the Interdecadal Pacific Oscillation on temperature and precipitation over the globe. Clim. Dyn. 45, 2667–2681 (2015).

    Article  Google Scholar 

  33. Deser, C., Knutti, R., Solomon, S. & Phillips, A. S. Communication of the role of natural variability in future North American climate. Nat. Clim. Change 2, 775–779 (2012).

    Article  ADS  Google Scholar 

  34. Lee, J.-Y. et al. in Climate Change 2021: The Physical Science Basis (eds Masson-Delmotte, V. et al.) 553–672 (IPCC, Cambridge Univ. Press, 2021).

  35. Hawkins, E. & Sutton, R. Time of emergence of climate signals. Geophys. Res. Lett. 39, L01702 (2012).

    Article  ADS  Google Scholar 

  36. IPCC Climate Change 2021: The Physical Science Basis (eds Masson-Delmotte, V. et al.) (Cambridge Univ. Press, 2021).

  37. Yao, T. et al. Recent Third Pole’s rapid warming accompanies cryospheric melt and water cycle intensification and interactions between monsoon and environment: multidisciplinary approach with observations, modeling, and analysis. Bull. Am. Meteorol. Soc. 100, 423–444 (2019).

    Article  ADS  Google Scholar 

  38. Zhou, T. & Zhang, W. Anthropogenic warming of Tibetan Plateau and constrained future projection. Environ. Res. Lett. 16, 044039 (2021).

    Article  ADS  Google Scholar 

  39. You, Q. et al. Warming amplification over the Arctic Pole and Third Pole: trends, mechanisms and consequences. Earth Sci. Rev. 217, 103625 (2021).

    Article  Google Scholar 

  40. Yin, H., Sun, Y. & Donat, M. G. Changes in temperature extremes on the Tibetan Plateau and their attribution. Environ. Res. Lett. 14, 124015 (2019).

    Article  ADS  Google Scholar 

  41. Samset, B. H. et al. Climate impacts from a removal of anthropogenic aerosol emissions. Geophys. Res. Lett. 45, 1020–1029 (2018).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  42. Wilcox, L. J. et al. Accelerated increases in global and Asian summer monsoon precipitation from future aerosol reductions. Atmos. Chem. Phys. 20, 11955–11977 (2020).

    Article  ADS  CAS  Google Scholar 

  43. Pan, S. et al. Larger sensitivity of Arctic precipitation phase to aerosolthan greenhouse gas forcing. Geophys. Res. Lett. 47, e2020GL090452 (2020).

    Article  ADS  Google Scholar 

  44. Qian, Y. et al. Light-absorbing particles in snow and ice: measurement and modeling of climatic and hydrological impact. Adv. Atmos. Sci. 32, 64–91 (2015).

    Article  CAS  Google Scholar 

  45. Ménégoz, M. et al. Snow cover sensitivity to black carbon deposition in the Himalayas: from atmospheric and ice core measurements to regional climate simulations. Atmos. Chem. Phys. 14, 4237–4249 (2014).

    Article  ADS  Google Scholar 

  46. Usha, K. H., Nair, V. S. & Babu, S. S. Modeling of aerosol induced snow albedo feedbacks over the Himalayas and its implications on regional climate. Clim. Dyn. 54, 4191–4210 (2020).

    Article  Google Scholar 

  47. Sarangi, C. et al. Dust dominates high-altitude snow darkening and melt over high-mountain Asia. Nat. Clim. Change 10, 1045–1051 (2020).

    Article  ADS  Google Scholar 

  48. Li, P., Furtado, K., Zhou, T., Chen, H. & Li, J. Convection‐permitting modelling improves simulated precipitation over the central and eastern Tibetan Plateau. Q. J. R. Meteorol. Soc. 147, 341–362 (2021).

    Article  ADS  Google Scholar 

  49. Zhao, Y., Zhou, T., Li, P., Furtado, K. & Zou, L. Added value of a convection permitting model in simulating atmospheric water cycle over the Asian water tower. J. Geophys. Res. Atmos. 126, e2021JD034788 (2021).

    Article  ADS  Google Scholar 

  50. Rajeevan, M., Gadgil, S. & Bhate, J. Active and break spells of the Indian summer monsoon. J. Earth Syst. Sci. 119, 229–247 (2010).

    Article  ADS  Google Scholar 

  51. Hill, S. A., Sobel, A. H., Biasutti, M. & Cane, M. A. On the all‐India rainfall index and sub‐India rainfall heterogeneity. Geophys. Res. Lett. 49, e2021GL096541 (2022).

    Article  ADS  Google Scholar 

  52. Danielson, J. J. & Gesch, D. B. Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010). US Geological Survey Open-File Report 2011–1073 (US Geological Survey, 2011).

  53. Schneider, U. et al. GPCC’s new land surface precipitation climatology based on quality-controlled in situ data and its role in quantifying the global water cycle. Theor. Appl. Climatol. 115, 15–40 (2014).

    Article  ADS  Google Scholar 

  54. Harris, I., Jones, P. D., Osborn, T. J. & Lister, D. H. Updated high-resolution grids of monthly climatic observations – the CRU TS3.10 Dataset. Int. J. Climatol. 34, 623–642 (2014).

    Article  Google Scholar 

  55. Yatagai, A. et al. APHRODITE: constructing a long-term daily gridded precipitation dataset for Asia based on a dense network of rain gauges. Bull. Am. Meteorol. Soc. 93, 1401–1415 (2012).

    Article  ADS  Google Scholar 

  56. Wu, J., Gao, X., Giorgi, F. & Chen, D. Changes of effective temperature and cold/hot days in late decades over China based on a high resolution gridded observation dataset. Int. J. Climatol. 37, 788–800 (2017).

    Article  CAS  Google Scholar 

  57. Hou, A. Y. et al. The Global Precipitation Measurement mission. Bull. Am. Meteorol. Soc. 95, 701–722 (2014).

    Article  ADS  Google Scholar 

  58. Skofronick-Jackson, G. et al. The Global Precipitation Measurement (GPM) mission for science and society. Bull. Am. Meteorol. Soc. 98, 1679–1695 (2017).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  59. Wang, X., Tolksdorf, V., Otto, M. & Scherer, D. WRF‐based dynamical downscaling of ERA5 reanalysis data for High Mountain Asia: towards a new version of the High Asia Refined analysis. Int. J. Climatol. 41, 743–762 (2021).

    Article  Google Scholar 

  60. Huang, B. et al. Extended Reconstructed Sea Surface Temperature, Version 5 (ERSSTv5): upgrades, validations, and intercomparisons. J. Clim. 30, 8179–8205 (2017).

    Article  ADS  Google Scholar 

  61. Harada, Y. et al. The JRA-55 reanalysis: representation of atmospheric circulation and climate variability. J. Meteorol. Soc. Jpn. Ser. II 94, 269–302 (2016).

    Article  ADS  Google Scholar 

  62. Hersbach, H. et al. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 146, 1999–2049 (2020).

    Article  ADS  Google Scholar 

  63. Gillett, N. P. et al. The Detection and Attribution Model Intercomparison Project (DAMIP v1.0) contribution to CMIP6. Geosci. Model Dev. 9, 3685–3697 (2016).

    Article  ADS  Google Scholar 

  64. O’Neill, B. C. et al. The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6. Geosci. Model Dev. 9, 3461–3482 (2016).

    Article  ADS  Google Scholar 

  65. Seager, R., Naik, N. & Vecchi, G. A. Thermodynamic and dynamic mechanisms for large-scale changes in the hydrological cycle in response to global warming. J. Clim. 23, 4651–4668 (2010).

    Article  ADS  Google Scholar 

  66. Chou, C. & Lan, C.-W. Changes in the annual range of precipitation under global warming. J. Clim. 25, 222–235 (2012).

    Article  ADS  Google Scholar 

  67. Wei, W., Zhang, R., Wen, M. & Yang, S. Relationship between the Asian westerly jet stream and summer rainfall over Central Asia and North China: roles of the Indian monsoon and the South Asian high. J. Clim. 30, 537–552 (2017).

    Article  ADS  Google Scholar 

  68. Dong, B., Sutton, R. T., Shaffrey, L. & Harvey, B. Recent decadal weakening of the summer Eurasian westerly jet attributable to anthropogenic aerosol emissions. Nat. Commun. 13, 1148 (2022).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  69. Allen, M. R. & Stott, P. A. Estimating signal amplitudes in optimal fingerprinting, part I: theory. Clim. Dyn. 21, 477–491 (2003).

    Article  Google Scholar 

  70. Allen, M. R. & Tett, S. F. B. Checking for model consistency in optimal fingerprinting. Clim. Dyn. 15, 419–434 (1999).

    Article  Google Scholar 

  71. Hasselmann, K. Optimal fingerprints for the detection of time-dependent climate change. J. Clim. 6, 1957–1971 (1993).

    Article  ADS  Google Scholar 

  72. Ribes, A., Planton, S. & Terray, L. Application of regularised optimal fingerprinting to attribution. Part I: method, properties and idealised analysis. Clim. Dyn. 41, 2817–2836 (2013).

    Article  Google Scholar 

  73. Ribes, A. & Terray, L. Application of regularised optimal fingerprinting to attribution. Part II: application to global near-surface temperature. Clim. Dyn. 41, 2837–2853 (2013).

    Article  Google Scholar 

  74. Zhang, X. et al. Detection of human influence on twentieth-century precipitation trends. Nature 448, 461–465 (2007).

    Article  ADS  CAS  PubMed  Google Scholar 

  75. Sun, Y. et al. Rapid increase in the risk of extreme summer heat in Eastern China. Nat. Clim. Change 4, 1082–1085 (2014).

    Article  ADS  Google Scholar 

  76. Mann, H. B. Nonparametric tests against trend. Econometrica 13, 245–259 (1945).

    Article  MathSciNet  MATH  Google Scholar 

  77. Kendall, M. G. Rank Correlation Methods (Charles Grifin, 1975).

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Acknowledgements

This work is jointly supported by the National Natural Science Foundation of China (grant no. 41988101), the second Tibetan Plateau Scientific Expedition and Research (STEP) programme (grant no. 2019QZKK0102), and the Strategic Priority Research Program of the Chinese Academy of Sciences (grant no. XDA20060102). J.J. is supported by the National Natural Science Foundation of China (grant no. 42205034) and the Special Research Assistant Project of the Chinese Academy of Sciences. F.S. is supported by the National Natural Science Foundation of China (grant no. 42175029). Y.Q. acknowledges support by the US Department of Energy (DOE), Office of Science and Office of Biological and Environmental Research (BER), as part of the Earth and Environmental System Modeling programme. The Pacific Northwest National Laboratory (PNNL) is operated for the DOE by the Battelle Memorial Institute under contract DE-AC05-76RLO1830. We also acknowledge support from the Jiangsu Collaborative Innovation Center for Climate Change.

Author information

Authors and Affiliations

  1. State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

    Jie Jiang, Tianjun Zhou, Xiaolong Chen & Wenxia Zhang

  2. University of the Chinese Academy of Sciences, Beijing, China

    Tianjun Zhou

  3. Pacific Northwest National Laboratory, Richland, WA, USA

    Yun Qian & Ziming Chen

  4. Max Planck Institute for Meteorology, Hamburg, Germany

    Chao Li & Hongmei Li

  5. Frontier Science Center for Deep Ocean Multispheres and Earth System and Physical Oceanography Laboratory, Ocean University of China, Qingdao, China

    Fengfei Song

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Contributions

T.Z. conceived and designed the study, with support from J.J. and Y.Q. J.J. conducted the analysis and drafted the manuscript. T.Z., Y.Q., C.L., F.S., H.L., X.C., W.Z. and Z.C. provided comments and revised the manuscript. Y.Q., C.L. and H.L. edited the paper. All the authors contributed to the scientific interpretation of the results.

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Correspondence to Tianjun Zhou.

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Extended data figures and tables

Extended Data Fig. 1 Long-term changes in summer precipitation in and around HMA.

a-f, The linear trends of summer precipitation (mm·month−1·decade−1) in and around HMA derived from the GPCC (a), CRU (b), CN05.1 (c), APHRO (d), JRA-55 (e) and ERA-5 data (f). The stippling indicates that the trend is significant at the 10% level according to the Mann-Kendall test; numbers denote the periods used to calculate the trends. The boundaries of HMA are identified as an isoline of 2500 m according to Global Multi-resolution Terrain Elevation Data 201052. g, h, Time series of 9-year running mean precipitation anomalies (mm·month−1) relative to a period from 1995–2014 over northern HMA (g, northern box in a) and southeastern HMA (h, southern box in a) derived from GPCC (black), CRU (gray), CN05.1 (orange), APHRO (blue), GPM (red, 2000–2020) and HAR data (purple, 1980–2020).

Source Data

Extended Data Fig. 2 The two leading modes of summer precipitation over HMA.

a-f, The two leading modes (EOF1 and EOF2) based on an EOF analysis of the 9-year running mean of summer precipitation (mm·month−1) in and around HMA derived from GPCC (a, b), CRU (c, d) and APHRO data (e, f). Numbers denote the corresponding explained variances. The boundaries of HMA are identified as an isoline of 2500 m according to Global Multi-resolution Terrain Elevation Data 201052. g, h, The corresponding time series of the two leading modes (PC1 and PC2) derived from GPCC (black), CRU (red) and APHRO data (blue).

Source Data

Extended Data Fig. 3 The mechanisms of the long-term trend in the westerly associated precipitation pattern over HMA in summer.

a-b, The linear trends of the moisture budget terms (mm·month−1·decade−1) associated with the first mode averaged over northern (a) and southeastern (b) HMA from 1958-2020. c-g, Linear trends of the vertical advection term (c, mm·month−1·decade−1), vertical velocity (d, Pa·s−1·decade−1, negative values denote upward motion), geostrophic relative vorticity (e, shading in units of 10−6·s−1·decade−1), and horizontal wind at 500 hPa (f, vector in units of m·s−1·decade−1) associated with the first mode from 1958 to 2020. The vector in e denotes the climatological (1995–2014) horizontal wind (m·s−1) at 500 hPa; the shading in f denotes the climatological temperature (K) at 500 hPa. g, Climatological zonal wind at 200 hPa. The box denotes the core zone of the subtropical westerly jet (SWJ). Above results are derived from JRA-55. The boundaries of HMA are identified as an isoline of 2500 m according to Global Multi-resolution Terrain Elevation Data 201052. h, Time series of 9-year running mean location (°; black) and strength (m·s−1; blue) of the jet axis derived from JRA-55 (solid lines) and ERA-5 (dashed lines). The latitude position with maximal wind speed averaged over 40° ~ 120°E denotes the location of the jet axis.

Source Data

Extended Data Fig. 4 Long-term changes in SWJ over HMA under different external forcings.

a, The linear trends of the SWJ strength index (m·s−1·decade−1; see Methods) derived from JRA-55 (1958–2020) and ERA-5 (1959–2020) associated with the first mode and the multimodel ensemble mean under ALL, NAT, GHG and AA forcings (1951–2020). Error bars denote the 25–75% range of all models (n = 10). b-e, The linear trends of zonal wind at 200 hPa (b, c, m·s−1·decade−1) and tropospheric temperature (d, e, K·decade−1) from 1951 to 2020 under GHG (b, d) and AA (c, e) forcings derived from the multimodel average of 10 CMIP6 models. The stippling indicates that the trend is significant at the 10% level according to the Mann-Kendall test. The blue contours denote the climatological zonal wind (m·s−1) at 200 hPa from 1995–2014. f, g, The meridional gradient of the temperature trend in d and e. The boundaries of HMA are identified as an isoline of 2500 m according to Global Multi-resolution Terrain Elevation Data 201052.

Source Data

Extended Data Fig. 5 Long-term changes in summer precipitation over HMA under different external forcings.

a-d, The linear trends of precipitation (mm·month−1·decade−1) from 1951 to 2020 under ALL (a), AA (b), GHG (c) and NAT (d) forcings derived from the multimodel average of 10 CMIP6 models. The stippling indicates that the trend is significant at the 10% level according to the Mann-Kendall test. The boundaries of HMA are identified as an isoline of 2500 m according to Global Multi-resolution Terrain Elevation Data 201052.

Extended Data Fig. 6 Long-term changes in summer precipitation over HMA under all external forcings for models with different horizontal resolutions.

a-b, Linear trends of precipitation (mm·month−1·decade−1) from 1951–2020 under all external forcings derived from the average of a group of CMIP6 models with resolutions lower (a) and higher (b) than 1.25° × 1.25° (Extended Data Table 1). The stippling indicates that the trend is significant at the 10% level according to the Mann-Kendall test. c, The differences in the linear trends between the models with higher and lower resolutions. The boundaries of HMA are identified as an isoline of 2500 m according to Global Multi-resolution Terrain Elevation Data 201052.

Extended Data Fig. 7 The relationship between the monsoon-associated precipitation pattern and IPO.

a, c, The correlation coefficients between the internal component of area-averaged summer (JJAS) precipitation over the South Asian monsoon core zone and the internal component of gridded precipitation from 1951–2020 in ACCESS-ESM1-5 (a) and MIROC6 (c). Boxes denote the domains of the South Asian monsoon core zone and southeastern HMA. b, d, The correlation coefficients between the corresponding time series of the monsoon-associated precipitation pattern and the internal component of sea surface temperature from 1951–2020 in ACCESS-ESM1-5 (b) and MIROC6 (d). The correlation coefficients for individual members are calculated, and the averages are shown. The stippling indicates that more than 80% of the members agree with the signal. The boundaries of HMA are identified as an isoline of 2500 m according to Global Multi-resolution Terrain Elevation Data 201052.

Extended Data Fig. 8 Future changes in precipitation over HMA.

a-f, The linear trends of precipitation (mm·month−1·decade−1) under the SSP2-4.5 (a-c) and SSP5-8.5 (d-f) scenarios from 2021-2100 derived from the ensemble mean of ACCESS-ESM1-5 (a, d) and MIROC6 (b, e) and the multimodel average of 10 CMIP6 models (c, f). The stippling indicates that the trend is significant at the 10% level according to the Mann-Kendall test. The boundaries of HMA are identified as an isoline of 2500 m according to Global Multi-resolution Terrain Elevation Data 201052.

Extended Data Fig. 9 Future changes in precipitation and the subtropical westerly jet in the summer under the SSP2-4.5 scenario.

Linear trends of precipitation (a, c, e, g; mm·month−1·decade−1) and 200 hPa zonal wind (b, d, f, h; m·s−1·decade−1) under the SSP2-4.5 (a, b), SSP2-4.5-aer (c, d), SSP2-4.5-GHG (e, f), and SSP2-4.5-nat (g, h) scenarios from 2021-2100 derived from the ensemble mean of MIROC6. The stippling indicates that the trend is significant at the 10% level according to the Mann-Kendall test. The boundaries of HMA are identified as an isoline of 2500 m according to Global Multi-resolution Terrain Elevation Data 201052.

Extended Data Fig. 10 The performance of CMIP6 models on climatological precipitation over HMA.

The climatological mean of summer precipitation (mm·month−1) in and around HMA derived from multiple observations (GPCC, CRU, APHRO and GPM) and the historical simulations of 12 CMIP6 models from 1995–2014. For the GPM, the period of 2001–2020 is used. For each model, the results of the ensemble mean are shown. Numbers denote the pattern correlation coefficients of HMA precipitation between the corresponding model and GPCC data. The boundaries of HMA are identified as an isoline of 2500 m according to Global Multi-resolution Terrain Elevation Data 201052.

Extended Data Table 1 Information on the 10 CMIP6 models used for detection and attribution in this study and the corresponding information
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Jiang, J., Zhou, T., Qian, Y. et al. Precipitation regime changes in High Mountain Asia driven by cleaner air. Nature 623, 544–549 (2023). https://doi.org/10.1038/s41586-023-06619-y

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