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Past warm intervals inform the future South Asian summer monsoon

Nature volume 641, pages 653–659 (2025)Cite this article

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Abstract

In the future, monsoon rainfall over densely populated South Asia is expected to increase, even as monsoon circulation weakens1,2,3. By contrast, past warm intervals were marked by both increased rainfall and a strengthening of monsoon circulation4,5,6, posing a challenge to understanding the response of the South Asian summer monsoon to warming. Here we show consistent South Asian summer monsoon changes in the mid-Pliocene, Last Interglacial, mid-Holocene and future scenarios, characterized by an overall increase in monsoon rainfall, a weakening of the monsoon trough-like circulation over the Bay of Bengal and a strengthening of the monsoon circulation over the northern Arabian Sea, as revealed by a compilation of proxy records and climate simulations. Increased monsoon rainfall is thermodynamically dominated by atmospheric moisture following the rich-get-richer paradigm, and dynamically dominated by the monsoon circulation driven by the enhanced land warming in subtropical western Eurasia and northern Africa. The coherent response of monsoon dynamics across warm climates reconciles past strengthening with future weakening, reinforcing confidence in future projections. Further prediction of South Asian summer monsoon circulation and rainfall by physics-based regression models using past information agrees well with climate model projections, with spatial correlation coefficients of approximately 0.8 and 0.7 under the high-emissions scenario. These findings underscore the promising potential of past analogues, bolstered by palaeoclimate reconstruction, in improving future South Asian summer monsoon projections.

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Fig. 1: Changes in the SASM during warm intervals.
Fig. 2: Thermodynamics and dynamics of South Asian summer rainfall changes during warm intervals.
Fig. 3: Surface warming during warm intervals and its effects on the SASM.
Fig. 4: Prediction of future changes in the SASM using past information.

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Data availability

The statistical operations and data visualization are based on Python 3.11.6, and the physical diagnostic analysis is based on NCL 6.6.2. All the maps are generated using PyNGL 1.6.1. The topographic elevation based on the Global 30 Arc-Second Elevation Data (GTOPO30) can be downloaded from https://svn-ccsm-inputdata.cgd.ucar.edu/trunk/inputdata/atm/cam/topo/. The CMIP6 outputs used in this study are available from https://esgf-data.dkrz.de/search/cmip6-dkrz/. The observed SST data used in the atmospheric general circulation experiments are available from https://svn-ccsm-inputdata.cgd.ucar.edu/trunk/inputdata/atm/cam/sst/. The mid-Pliocene boundary conditions can be downloaded from https://geology.er.usgs.gov/egpsc/prism/7.2_pliomip2_data.html. The outputs of numerical model experiments and information on proxy records are available from https://doi.org/10.5281/zenodo.15001239 (ref. 109).

Code availability

The script for the adjustment of the palaeo-calendar effect (PaleoCalAdjust) is available from https://github.com/pjbartlein/PaleoCalAdjust. The CESM1.2 script can be downloaded from https://www2.cesm.ucar.edu/models/cesm1.2/. The scripts used to generate all figures and to perform moisture budget and moist static energy equation analyses are available from https://doi.org/10.5281/zenodo.15001239 (ref. 109).

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Acknowledgements

This work is supported by the National Natural Science Foundation of China Excellent Research Group for Tibetan Plateau Earth System (continuation grant) and the Second Tibetan Plateau Scientific Expedition and Research (STEP) programme (grant number 2019QZKK0102). L.H. also acknowledges support from NSF AGS 23-17159. The atmospheric general circulation model experiments were performed at the Earth System Numerical Simulation Facility (EarthLab) supported by the National Large Scientific and Technological Infrastructure project. We acknowledge the climate modelling groups that produced and provided the model simulations used in this study.

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Authors and Affiliations

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

    Linqiang He, Tianjun Zhou & Zhun Guo

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

    Linqiang He & Tianjun Zhou

  3. Lamont-Doherty Earth Observatory, Columbia University, Palisades, NY, USA

    Linqiang He

  4. Climate Change Research Center, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

    Zhun Guo

Authors
  1. Linqiang He
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  2. Tianjun Zhou
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Contributions

T.Z. conceived of and designed the study, with support from L.H. L.H. conducted the analysis and drafted the paper. T.Z. provided comments and revised the paper. L.H. and Z.G. performed the atmospheric general circulation model experiments. L.H. performed the linear baroclinic model experiment. 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 Moisture budget over South Asian during warm intervals.

a–d, Individual terms (unit, mm d−1) of the column-integrated moisture budget (see Methods; from left to right are changes in rainfall, evaporation, zonal moisture advection, meridional moisture advection, vertical moisture transport and residual term) in the mid-Pliocene (a), Last Interglacial (b), mid-Holocene (c), and SSP5-8.5 scenario (d) relative to the pre-industrial period, derived from CMIP6 simulations. Bars denote the multi-model ensemble mean and markers denote the individual model results.

Extended Data Fig. 2 Scatterplots of atmospheric variables related to thermodynamics and dynamics over South Asia during warm intervals.

a–f, Scatterplots of global mean surface temperature (unit, K) versus South Asian specific humidity at 850 hPa (a; unit, g kg−1), South Asian specific humidity versus the thermodynamic term (b; unit, mm d−1), meridional surface temperature contrast (unit, K) versus South Asian monsoon trough index (c; unit, m s−1), South Asian monsoon trough index versus the dynamic term (d; unit, mm d−1), meridional surface temperature contrast versus northern Arabian Sea monsoon index (e; unit, m s−1), northern Arabian Sea monsoon index versus the dynamic term (f), derived from CMIP6 simulations. The solid lines denote the linear regression obtained by the least squares method, and dashed curves indicate the 99% confidence range of the linear regression. The Pearson correlation coefficients (r) and p values are shown in the lower corner of each panel. These indexes are defined in the Methods.

Extended Data Fig. 3 West African monsoon during warm intervals and its effects on South Asian summer monsoon.

a–d, Multi-model means of summer rainfall (shading; unit, mm d−1) and horizontal winds at 850hPa (vectors; unit, m s−1) in the mid-Pliocene (a), Last Interglacial (b), mid-Holocene (c), and SSP5-8.5 (d) relative to the pre-industrial period, derived from CMIP6 simulations. The changes in rainfall and horizontal winds for which at least five out of six models and four out of five models have the same sign are shown, respectively. e,f, Spatial pattern of the idealized heating (shading; unit, K d−1) over northern tropical Africa at 0.45 sigma level (e) and response of horizontal wind (vector; unit, m s−1) at 850 hPa (f), derived from the linear baroclinic model experiment (see Methods; Extended Data Table 2). Only vector anomalies passing the 90% confidence level of the t-test are shown. The box indicates target regions for easterly anomalies. Blue lines represent the 2,000 m elevation contour based on the Global 30 Arc-Second Elevation Data50. The maps were generated using PyNGL.

Extended Data Fig. 4 Role of zonal winds in the dynamics of South Asian summer monsoon during warm intervals.

a–d, Multi-model means of climatological air temperature in the pre-industrial period (shading; unit, K) and changes in zonal winds (vector; unit, m s−1) at 850 hPa. e–h, Column-integrated sensible heat energy by zonal winds changes (see Methods; unit, W m−2). i–l, Changes in vertical velocity at 500 hPa (multiplied by −1; unit, 10−2 Pa s−1). The shading denotes changes in the mid-Pliocene (a,e,i), Last Interglacial (b,f,j), mid-Holocene (c,g,k), and SSP5-8.5 scenario (d,h,l) relative to the pre-industrial period, derived from CMIP6 simulations. Dashed contours represent the zero value. The changes for which at least four out of five models have the same sign are shown. Black lines represent the 2,000 m elevation contour based on the Global 30 Arc-Second Elevation Data50. The maps were generated using PyNGL.

Extended Data Fig. 5 Response of surface temperature and South Asian summer monsoon to different forcing agents.

a,c,e,g, Response of summer surface temperature (shading; unit, K) to the mid-Pliocene surface type (a), Last Interglacial orbital parameters (c), SSP5-8.5 atmospheric CO2 (e), and SSP5-8.5 SST (g), derived from the atmospheric model experiments based on CAM5 (see Methods; Extended Data Table 2). b,d,f,h, The same as in a,c,e and g, respectively, but for the summer precipitation (shading; unit, mm d−1) and horizontal winds at 850 hPa (vectors; unit, m s−1). Only anomalies passing the 90% confidence level of the t-test are shown. Blue boxes in the right panel denote the subregions for assessing the spatially non-uniform monsoon response. Blue lines represent the 2,000 m elevation contour based on the Global 30 Arc-Second Elevation Data50. The maps were generated using PyNGL.

Extended Data Fig. 6 Changes in surface temperature and South Asian monsoon rainfall under SSP2-4.5 and SSP3-7.0 scenarios.

a,c,d,e, Multi-model means of summer surface temperature (a; unit, K), rainfall (c; unit, mm d−1), thermodynamic term (d), and dynamic term (e) in the SSP2-4.5 relative to the pre-industrial period. b,f,g,h, The same as in a,c,d and e, respectively, but for changes in the SSP3-7.0 relative to the pre-industrial period, derived from CMIP6 simulations. Dashed contours represent the zero value. The changes for which at least four out of five models have the same sign are shown. Blue boxes indicate the target regions of the study (see Methods). Blue lines represent the 2,000 m elevation contour based on the Global 30 Arc-Second Elevation Data50. The maps were generated using PyNGL.

Extended Data Table 1 Details of CMIP6 warm interval simulations used in the study
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Extended Data Table 2 Details of the atmospheric model experiments based on CAM5
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Supplementary information

Supplementary Table 1

Information on the proxy records over South Asia used in the study, including the period, location, interpretation, proxy type and references.

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He, L., Zhou, T. & Guo, Z. Past warm intervals inform the future South Asian summer monsoon. Nature 641, 653–659 (2025). https://doi.org/10.1038/s41586-025-08956-6

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