# Atmospheric dynamic constraints on Tibetan Plateau freshwater under Paris climate targets

Tao Wang, Yutong Zhao, Chaoyi Xu, Philippe Ciais, Dan Liu, Hui Yang,

Shilong Piao, Tandong Yao

► To cite this version:

Tao Wang, Yutong Zhao, Chaoyi Xu, Philippe Ciais, Dan Liu, et al. Atmospheric dynamic constraints on Tibetan Plateau freshwater under Paris climate targets. Nature Climate Change, 2021, 82, pp.339 - 345. 10.1038/s41558-020-00974-8 . hal-03123544

HAL Id: hal-03123544

https://hal.science/hal-03123544v1

Submitted on 3 Oct 2025

**HAL** is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
<figure><img src="image_2.png" /></figure>

# Atmospheric dynamic constraints on Tibetan Plateau freshwater under Paris climate targets

Tao Wang<sup>1,2✉</sup>, Yutong Zhao<sup>1,3</sup>, Chaoyi Xu<sup>1,3</sup>, Philippe Ciais<sup>1,4</sup>, Dan Liu<sup>1</sup>, Hui Yang<sup>4</sup>, Shilong Piao<sup>1,2,5</sup> and Tandong Yao<sup>2,6</sup>

Rivers originating in the Tibetan Plateau provide freshwater to downstream populations, yet runoff projections from warming are unclear due to precipitation uncertainties. Here, we use a historical atmospheric circulation-precipitation relationship to constrain future modelled wet-season precipitation over the Tibetan Plateau. Our constraint reduces precipitation increases to half of those from the unconstrained ensemble and reduces spread by around a factor of three. This constrained precipitation is used with estimated glacier melt contributions to constrain future runoff for seven rivers. We estimate runoff increases of 1.0-7.2% at the end of the twenty-first century for global mean warming of 1.5-4 °C above pre-industrial levels. Because population projections diverge across basins, this runoff increase will reduce the population fraction living under water scarcity conditions in the Yangtze and Yellow basins but not in the Indus and Ganges basins, necessitating improved water security through climate change adaptation policies in these regions at higher risk.

The upstream discharges of the Indus, Brahmaputra, Ganges, Salween, Mekong, Yangtze and Yellow rivers originate from the Tibetan Plateau and the Himalayan range, areas known as Asia's water tower (Supplementary Table 1). It is important to be able to forecast how much freshwater from these upper basins will be available to supply the one-fifth of humanity living downstream<fnref n="1" />. The climate of the Asian water tower is characterized by the influence of atmospheric circulation systems from the Indian summer monsoon and mid-latitude westerlies²⁻⁴. The region stores water in glaciers and snow that can be mobilized into runoff under warmer conditions⁵⁻⁷. Yet precipitation is the dominant factor controlling historical river discharge in nearly all upstream basins, particularly during the wet season (June to September). The exception is the upper Indus basin where meltwaters from snow and glaciers dominates⁸⁻¹⁰. Earlier studies have addressed the importance of South Asian monsoon-induced change in precipitation¹¹, warming-induced changes in evapotranspiration⁹,¹² and melting snow and ice⁷,¹³ on the spatially variable responses of runoff changes in the past several decades¹⁴,¹⁵. A coherent picture of the extent to which climate change will affect river runoff in Asia's water towers for global mean warming of 1.5 and 2 °C (the Paris climate targets) and a higher warming of 4 °C above pre-industrial years (1850-1900) has yet to emerge. Addressing this question is complicated by the fact that there is substantial uncertainty in precipitation projections from climate models¹⁶⁻¹⁸, including changes in sign for some basins¹⁶. In addition, there are also uncertainties in modelled glaciers and snow melt. We attempt to advance our current understanding of wet-season runoff projections in Asia's water towers by placing observational constraints on large uncertainties in model projections especially for precipitation under different warming levels and to evaluate whether there exists a potential threat of water scarcity to the people reliant on upstream water resources for their livelihoods.

We used projections from Earth System Models (ESMs) (Supplementary Table 2) archived in the Coupled Model Intercomparison Project Phases 5 and 6 (CMIP5 and CMIP6) to estimate the responses of total runoff to global temperature increases of 1.5, 2 and 4 °C. We selected ESMs on the basis of their global temperature increase from the pre-industrial period (1850-1900) until the end of this century (2070-2099). For the 1.5 °C case, we selected ten ESMs from the CMIP5 representative concentration pathway RCP 2.6 and CMIP6 shared socioeconomic pathways SSP 1-2.6 with temperature increases within the range of 1.3-1.7 °C. For the 2 °C case we used 17 ESMs from RCP 4.5 and SSP 2-4.5 scenarios with temperature increases in the 1.8-2.2 °C range, while for the 4 °C case we used 11 ESMs from RCP 8.5 and SSP 5-8.5 scenarios with temperature increases in the range 3.6-4.4 °C (Methods). Global warming of 1.5 °C above the pre-industrial level gives a regional warming of 1.9 ± 0.3 °C in the upper basins sourced from the Tibetan Plateau (Extended Data Fig. 1), while warming of 2 °C and 4 °C leads to regional warming of 2.8 ± 0.4 °C and 5.4 ± 0.6 °C respectively. Such warming amplification is typical of high-mountain environments¹⁹. Between the historical period (1960-2005) and the end of this century (2070-2099), the total wet-season runoff in all the upper basins, except the Indus, is projected by the models to increase by 3.1% (1.5 °C), 5.3% (2 °C) and 16.7% (4 °C) (Supplementary Fig. 1). For the Indus, the simulated decrease in runoff can be partly explained by a decrease in precipitation during the cold season (January to May) and an increase in evapotranspiration (Supplementary Figs. 2a and 3a).

Although runoff projections from CMIP ESMs provide plausible future responses to warming, CMIP models have high biases and large spread in historical wet-season runoff (Supplementary Fig. 4), which urges caution in the direct use of model projections. Specifically, the projected runoff changes over all upper basins at the end of this century relative to the historical period vary from -8.6% to +15.1% at 1.5 °C of global warming. For 2 °C and 4 °C of

<footnote n="1">¹Key Laboratory of Alpine Ecology, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China. ²CAS Center for Excellence in Tibetan Plateau Earth Sciences, Chinese Academy of Sciences, Beijing, China. ³University of Chinese Academy of Sciences, Beijing, China. ⁴Laboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, Gif-sur-Yvette, France. ⁵Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, China. ⁶Key Laboratory of Tibetan Environment Changes and Land Surface Processes, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China. ⁷e-mail: twang@itpcas.ac.cn</footnote>
<figure><img src="image_3.png" /><figcaption>**Fig. 1** | Simulated and constrained projections of wet-season precipitation change at different warming levels. **a–f**, The multimodel ensemble mean precipitation difference (as a percentage) between historical period (1960–2005) and end of this century (2070–2099) at different temperature levels over the river basins of the upper Indus (**a**), Ganges (**b**), Brahmaputra (**c**), Salween and Mekong (**d**), Yangtze (**e**) and Yellow (**f**). The number of CMIP5 and CMIP6 ESMs used to calculate precipitation changes is six and four for 1.5 °C of global warming (13 and four for 2 °C and six and five for 4 °C). The dark grey regions denote the geographical locations of each river basin. Error bars indicate one standard deviation of intermodel spread. The solid and stippled bars represent model simulated and constrained precipitation change, respectively. Blue, yellow and red represent the 1.5, 2 and 4 °C, respectively. The wet season here is defined as the period from June to September.</figcaption></figure>

warming, the ranges are -7.7% to +16.0% and -10.3% to +33.8%, respectively. Explanations for such large uncertainties include differences in the projected change in precipitation and variations in the responses of terrestrial evapotranspiration to warming. A better understanding of the relative contributions of precipitation and evapotranspiration uncertainties is important for understanding how much of the uncertainty in projected runoff changes could be removed through improvements to the simulated precipitation and evapotranspiration of current ESMs. To this end, we partitioned the intermodel uncertainty of projected runoff changes into uncertainties due to changes in precipitation, those due to changes in evapotranspiration and a residual term (for example, snow and soil water storage), between the historical period and the end of this century. Except in the upper Indus, precipitation changes are more important for explaining the model spread in projected runoff changes during the wet season, with the proportion of variance explained ranging from 61 to 98% across basins under all warming

levels (Extended Data Fig. 2). In the upper Indus, wet-season precipitation changes have a low explained variance in the wet-season runoff spread, while changes in January-to-September precipitation mainly explain such runoff spread (Extended Data Fig. 3), highlighting a large role of cold-season precipitation in wet-season runoff over the upper Indus. The contribution of wet-season evapotranspiration to the spread in runoff change is marginal and does not exceed 15% across all upper basins (Extended Data Fig. 2). By constraining the precipitation changes, we could reduce the spread of future wet-season runoff changes.

Yet, CMIP5 and CMIP6 models have substantial biases in wet-season precipitation during the historical period (Supplementary Fig. 5) which could project strongly onto change under warming, creating large uncertainties in future projections. To understand the physical reasons inducing poor historical simulations of precipitation, we decomposed historical precipitation changes into dynamic (atmospheric circulation change) and
<figure><img src="image_4.png" /><figcaption>**Fig. 2** | The intermodel relationship between runoff changes and precipitation changes at different warming levels. **a–f**, The intermodel relationship between simulated runoff changes (ΔR) and precipitation changes (ΔP) across CMIP5 and CMIP6 models, and the probability density functions of runoff changes in the river basins of the upper Indus (a), Ganges (b), Brahmaputra (c), Salween and Mekong (d), Yangtze (e) and Yellow (f). In the upper Indus basin, the period from January to September is used to establish the intermodel relationship, while the period from June to September is considered in other upper basins. Robust linear regression analysis is applied to explore the intermodel relationship between precipitation changes and runoff changes. The dotted lines represent the prior probability density function (PDF) of runoff changes and the coloured lines showed the three constrained PDFs of runoff changes. Each dot represents an ESM from either CMIP5 or CMIP6 and the number of CMIP5 and CMIP6 ESMs is six and four for 1.5 °C of global warming (13 and four for 2 °C and six and five for 4 °C). Blue, yellow and red represent the 1.5, 2 and 4 °C, respectively. ** and *** indicate that the coefficient is significant at *P* < 0.05 and *P* < 0.01, respectively.</figcaption></figure>

thermodynamic (moisture increase due to warming) components on the basis of the atmospheric moisture budget (Methods). The results based on the Japanese 55-yr reanalysis data (JRA-55), whose wet-season precipitation is highly consistent with Global Precipitation Climatology Centre Full Re-Analysis Version 2018 (GPCC) precipitation data (R=0.79, P<0.01) (Supplementary Fig. 6), showed that atmospheric circulation changes, instead of the thermodynamic component, dominate wet-season precipitation variability over the Tibetan Plateau for the historical period 1960–2005 (Extended Data Fig. 4). This result is consistent with increasing evidence that the sources of atmospheric water vapour over the Tibetan Plateau are predominantly provided by Asia's

monsoon systems and mid-latitude westerlies²⁻⁴. Compared to results from JRA-55, CMIP models significantly underestimate the role of atmospheric dynamics and the importance of atmospheric circulation changes have a large intermodel difference (Extended Data Fig. 4), which become important sources of large biases and intermodel spread in historical precipitation simulations. To quantify atmospheric circulation aspects of historical precipitation changes, we further analysed spatial correlation patterns of atmospheric circulation (sea-level pressure, SLP) at 5° resolution with precipitation over Asia's water tower for the historical period (Methods). On the interannual timescale, the correlations between precipitation and SLP in July do show
<figure><img src="image_5.png" /><figcaption>**Fig. 3** | Simulated and constrained projections of wet-season runoff change at different warming levels. **a–f**, The multimodel ensemble mean runoff difference (as a percentage) between the periods 1960–2005 and 2070–2099 at different temperature levels over the river basins of the upper Indus (a), Ganges (b), Brahmaputra (c), Salween and Mekong (d), Yangtze (e) and Yellow (f). The number of CMIP5 and CMIP6 ESMs used to calculate runoff changes is six and four for 1.5 °C of global warming (13 and four for 2 °C and six and five for 4 °C). Error bars indicate one standard deviation of intermodel spread. The solid and stippled bars represent model simulated and constrained runoff change, respectively, and the light blue bars stand for the glacier melt runoff (Methods). Blue, yellow and red represent the 1.5, 2 and 4 °C, respectively.</figcaption></figure>

significant values in the regions between the Bay of Bengal and the Indo-China Peninsula, and from the North Atlantic ocean to northern Europe (Supplementary Fig. 7), confirming the influence of monsoonal circulation via ocean–atmosphere interaction²⁰ and the contribution of mid-latitude westerlies through the North Atlantic Ocean–Europe–Asia teleconnection²¹,²². We then reconstructed wet-season precipitation on the basis of SLP (Methods) and found that the explained variance in wet-season precipitation, when using different precipitation products, could reach as high as 92–95% (Supplementary Fig. 8).

The strong link between Tibetan precipitation and SLP diagnosed by our statistical models provides an opportunity to constrain model projections of future precipitation changes. The assumption here is that the precipitation–circulation relationship diagnosed in the historical period is approximately stationary under climate change. Since there are no observations of future climate, we used

CMIP model outputs to test the realism of this assumption. To this end, we investigated whether ESM-simulated changes in precipitation can be explained by combining changes in SLP with each ESM's own precipitation–SLP relationship. Each ESM's simulated change in precipitation (ΔP), for the future period 2006–2099, was plotted against a predicted change in precipitation constructed as ΔP = f(P, SLP)ΔSLP, where f(P,SLP) is each model's own precipitation–SLP relationship in the historical period 1960–2005 and ΔSLP is each ESM's future SLP change relative to the historical period. Over the Tibetan Plateau, the simulated and predicted precipitation changes, ΔP and ΔP, agreed well for the three different warming levels (Extended Data Fig. 5 and Supplementary Figs. 9 and 10), with explained variances of 89, 92 and 92% in terms of multimodel mean ensemble. Thus, the historical precipitation–SLP relationship is an important driver of future precipitation changes, given a particular future SLP.
<figure><img src="image_6.png" /><figcaption>**Fig. 4** | Projected changes in percentage of population living above the absolute water scarcity level at different warming levels. The bars denote the percentage of the population living in the highly dependent area that could be satisfied by wet-season upstream runoff for a full year at the absolute water scarcity level (500 m³ per person per year) and brown points represent the population (in millions) integrated over the highly dependent area. The highly dependent area is defined as the area where population over the basin is highly dependent on upstream water resources for their livelihoods (Methods). The population projections are taken from the SSPs calculated as: an average of the populations from SSP1 and SSP 4 (1.5 °C), the population from SSP 2 (2 °C) and the average of the populations from SSP 3 and SSP 5 (4 °C). White lines are boundaries of the entire Indus, Brahmaputras, Ganges, Salween and Mekong, Yangtze and Yellow river basins. The Tibetan Plateau is in dark grey, with its upstream basins indicated by dark blue. The light blue denotes the downstream area that is delineated by the transit time of wet-season runoff from the upstream area being <10 d. Blue, yellow and red represent the 1.5, 2 and 4 °C, respectively. Note that the population decline over Yangtze and Yellow river basins in the late twenty-first century is primarily attributed to their reduced fertility rates, with a substantial population decline in rural areas and a relatively stable population in urban areas²⁹.</figcaption></figure>

These results give confidence that an observationally derived precipitation-SLP relationship could be applied to individual ESM projections of ΔSLP to ultimately constrain ΔP. We therefore created an empirical regression model between precipitation and SLP across the globe using the historical observations (1960-2005) and we computed constrained precipitation changes by forcing the empirical regression model with bias-corrected SLP data simulated by CMIP ESMs during 2006-2099 under different warming levels (Methods). Our results indicated that, for a global warming level of 1.5°C, the constrained wet-season precipitation increases by 2.3 ± 1.2% at the end of this century for all upper basins, less than half of the increase of the unconstrained projection (5.2 ± 3.4%) and with a spread reduced by around a factor of three. We found a similar magnitude of spread reduction but estimated that the projected precipitation increases become larger at higher warming levels, with 3.8 and 7.8% for global mean warming of 2 and 4°C, respectively (Fig. 1). The constrained spatial distribution of precipitation change was also compared to the original distribution (Extended Data Fig. 6 and Supplementary Figs. 11–13). In contrast to the original distribution, there is a sharper north-to-south wetting gradient, with a strong wetting over the upper Ganges basin (3.1, 8.4 and 16.1%, corresponding to 1.5, 2 and 4°C respectively) and Brahmaputra basin (2.1, 4.5 and 9.3%) in the southern Tibetan Plateau and a relatively weaker wetting over upper Yellow (0.7, 1.1 and 2.7%) basin in the northwestern region (Extended Data Fig. 6). Such a pronounced gradient is consistent with the evidence that the influence of the

South Asian monsoon, which is a major driver of wet-season precipitation changes, decreases as one moves northwards over the Tibetan Plateau⁴.

Next, we addressed how to constrain projected runoff changes using observationally constrained precipitation changes. Since precipitation changes are important for explaining the model spread in projected runoff changes (Extended Data Fig. 2), we then explored the intermodel relationship between precipitation change and runoff change, and applied that relationship to the observationally constrained precipitation change to provide constrained runoff change projections under specific warming levels for each upper basin. We found a strong correlation between precipitation change and runoff change during the wet season (June to September) except in the upper Indus where the best predictor of runoff is precipitation from January to September (Fig. 2), consistent with previous studies highlighting the importance of winter water storage of the discharge of this river⁸,¹⁶,²³,²⁴.

The observationally constrained runoff change between the historical period and the end of this century across all upper river basins except the upper Indus is 1.6 ± 2.5% for 1.5 °C of global warming (3.2 ± 2.3% for 2 °C and 7.3 ± 3.1% for 4 °C) (Fig. 3). This constraint implies that the range of uncertainty was reduced by more than a factor of three, with projected runoff increases that are also nearly half of those suggested by the original ESM simulations. In the upper Indus, both the original and observationally constrained projections suggest a runoff decrease, with the decline
in the original projections two to four times greater than that in the constrained ones for 1.5°C to 4°C warming (Fig. 3). Furthermore, to take into account the contribution of glacier melt to runoff, we used existing monthly simulation results over all individual glaciers from a glacier mass balance model which is forced by downscaled climate projections under the three emission scenarios⁵. The results showed that glacier melt runoff (as a percentage of historical runoff) adds -0.3, 0.9 and 1.4% to the precipitation-constrained runoff change, resulting in a total runoff increase over all upper basins of 1.0% at 1.5°C, 4.1% at 2°C and 7.2% at 4°C (Fig. 3). The importance of glaciers melt varied substantially across upper basins. In the upper Indus, glacier melt-induced runoff increase (0.2%) is not sufficient to compensate for precipitation-constrained runoff decrease (-2.1%) at 1.5°C but the increase (4.5 and 7.9%) will override the decline in precipitation-constrained runoff (-3.0 and -6.1%) at 2 and 4°C, respectively. In contrast, the contribution of glaciers is smaller than that of precipitation in the upper Ganges, Yellow and Brahmaputra river basins. We note that the precipitation data used to force the glacier mass balance model⁵ is not observationally constrained, although this inconsistency might not lead to a large bias of glacier runoff since glaciers tend to be more sensitive to temperature change than to changes in precipitation¹¹,²⁵.

Will a general increase in upstream runoff mitigate water scarcity conditions? To attempt to answer this question, we initially delineated the area where the population is highly dependent upon upstream water resources (thereafter referred to as the highly dependent area) for each basin. The highly dependent area includes the entire upstream basin and a certain fraction of the downstream region which was determined using the criterion that the travel time of wet-season runoff through the area is <10 d (Methods). We then applied a commonly used measure of water scarcity (the water crowding indicator²⁶; Methods) to estimate projected changes in the percentage of the population that would fall into different water scarcity classes in the highly dependent area by selecting feasible combinations of population projections from the SSPs and global warming levels (Methods).

Much of the population growth by the end of this century will occur in the highly dependent areas of the Indus, Brahmaputra and Ganges basins. However, the population is projected to decline in the highly dependent area of the Yangtze and Yellow rivers, with a decrease by a factor of two between 2006 and 2099 (Fig. 4). Following Prichard²⁷, we estimated that the net volume of historical wet-season upstream runoff in the Indus, Brahmaputra and Ganges basins could allow ~60–78% of population from highly dependent areas to live above the absolute water scarcity level (>500 m³ per person per year), below which widespread water restrictions are needed²⁸, for a full year (Fig. 4a–c). This volume could only satisfy ~30–40% of the population living above the chronic water scarcity level (>1,000 m³ per person per year) (Extended Data Fig. 7). Despite a projected increase in upstream runoff (Fig. 3a–c), the percentage of the population which upstream runoff could supply at the absolute-scarcity level will become stable or even decline with warming of 1.5°C compared to the historical period (Fig. 4a–c). Global warming of 2°C or 4°C will further reduce this proportion of the population by up to 30% in the Indus basin (Fig. 4a), owing to its large and rapidly growing populations, suggesting an aggravation of water scarcity conditions. Our result is consistent with a recent finding that the rapid projected population and economic growth would aggravate the already high baseline water stress in the upper Indus¹. However, the role of glaciers in providing water to exposed populations over the Indus basin²⁷ should not be neglected. For example, in a climatological year (averaged over end of this century), the volume of wet-season glacier runoff, with 1.5°C warming, is 10.9×10⁹ m³ rising to 12.9×10⁹ m³ with 4°C warming. These runoff volumes could satisfy the annual water needs of 21.8 and 25.8 million people—over 19 and 20% of the population in the

highly dependent area of the Indus basin—living above the absolute water scarcity level.

By contrast, in the Yangtze and Yellow basins, the volume of upstream runoff could allow a nearly twofold increase in the proportion of the population from highly dependent areas living above the absolute-scarcity (Fig. 4d,e) and chronic-scarcity categories (Extended Data Fig. 7d,e) for a full year at 1.5°C compared to the historical period. This turnaround is mainly due to a nearly halving of the total population assumed in the SSP scenarios (Fig. 4d,e). As a result, there is increasing per-capita water availability for human uses in both the Yangtze and Yellow river basins, helping to relieve the previous chronic and absolute water scarcity conditions respectively. Despite an overall increasing trend in river runoff, a divergence of future changes in water scarcity conditions is observed across basins, with a deterioration in the highly dependent areas of the Indus and Ganges basins but a substantial improvement in those of the Yangtze and Yellow river basins.

In summary, we built an empirical relationship between Tibetan precipitation and SLP, as a surrogate for large-scale atmospheric circulation, to constrain future precipitation projections that are the largest source of uncertainties in forecasts of future runoff changes and their associated hydrological impacts. The importance of precipitation changes in future wet-season runoff changes was demonstrated for all basins except for the Indus river basin, where glacier meltwater also plays an almost equal role. Our constraints substantially increase confidence that warming will lead to a long-term increase in runoff over Asia's water tower. However, rising water demands, indicated by population growth, outweigh warming in defining the water scarcity level to the end of this century in the Indus and Brahmaputra basins, particularly at higher warming levels and a large proportion of the population will continue to experience severe water stress. Practical climate change adaptation measures such as increasing crop use efficiency and dam regulations¹ should be sought in these basins to secure future water, food security and environmental sustainability under the Paris climate targets.

## Online content

Any methods, additional references, Nature Research reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at https://doi.org/10.1038/s41558-020-00974-8.

Received: 3 November 2019; Accepted: 1 December 2020;

Published online: 11 January 2021

## References

1. Immerzeel, W. W. et al. Importance and vulnerability of the world's water towers. *Nature* **577**, 364–369 (2020).

2. Tian, L. et al. Stable isotopic variations in west China: a consideration of moisture sources. *J. Geophys. Res.* **112**, D10112 (2007).

3. Schiemann, R., Lüthi, D. & Schär, C. Seasonality and interannual variability of the westerly jet in the Tibetan Plateau region. *J. Clim.* **22**, 2940–2957 (2009).

4. Yao, T. et al. A review of climatic controls on δ¹⁸O in precipitation over the Tibetan Plateau: observations and simulations. *Rev. Geophys.* **51**, 525–548 (2013).

5. Huss, M. & Hock, R. Global-scale hydrological response to future glacier mass loss. *Nat. Clim. Change* **8**, 135–140 (2018).

6. Shea, J. M. & Immerzeel, W. W. An assessment of basin-scale glaciological and hydrological sensitivities in the Hindu Kush-Himalaya. *Ann. Glaciol.* **57**, 308–318 (2016).

7. Kraaijenbrink, P. D. A., Bierkens, M. F. P., Lutz, A. F. & Immerzeel, W. W. Impact of a global temperature rise of 1.5 degrees Celsius on Asia's glaciers. *Nature* **549**, 257–260 (2017).

8. Immerzeel, W. W., Van, B. L. P. & Bierkens, M. F. Climate change will affect the Asian water towers. *Science* **328**, 1382–1385 (2010).
9. Bookhagen, B. & Burbank, D. W. Toward a complete Himalayan hydrological budget: spatiotemporal distribution of snowmelt and rainfall and their impact on river discharge. *J. Geophys. Res.* F 115, F03019 (2010).

10. Mukhopadhyay, B. & Khan, A. A reevaluation of the snowmelt and glacial melt in river flows within upper Indus basin and its significance in a changing climate. *J. Hydrol.* **527**, 119–132 (2015).

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

12. Yang, K. et al. Response of hydrological cycle to recent climate changes in the Tibetan plateau. *Climatic Change* **109**, 517–534 (2011).

13. Yang, W., Guo, X., Yao, T., Zhu, M. & Wang, Y. Recent accelerating mass loss of southeast Tibetan glaciers and the relationship with changes in macroscale atmospheric circulations. *Clim. Dynam.* **47**, 805–815 (2016).

14. Cuo, L., Zhang, Y., Zhu, F. & Liang, L. Characteristics and changes of streamflow on the Tibetan Plateau: a review. *J. Hydrol.* **2**, 49–68 (2014).

15. Wang, Y. et al. Contrasting runoff trends between dry and wet parts of eastern Tibetan Plateau. *Sci. Rep.* **7**, 15458 (2017).

16. Lutz, A. F., Immerzeel, W. W., Shrestha, A. B. & Bierkens, M. F. P. Consistent increase in high Asia's runoff due to increasing glacier melt and precipitation. *Nat. Clim. Change* **4**, 587–592 (2014).

17. Lutz, A. F., Immerzeel, W. W., Kraaijenbrink, P. D., Shrestha, A. B. & Bierkens, M. F. Climate change impacts on the upper Indus hydrology: sources, shifts and extremes. *PLoS ONE* **11**, e0165630 (2016).

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

19. Pepin, N. et al. Elevation-dependent warming in mountain regions of the world. *Nat. Clim. Change* **5**, 424–430 (2015).

20. Turner, A. G. & Annamalai, H. Climate change and the South Asian summer monsoon. *Nat. Clim. Change* **2**, 587–595 (2012).

21. Schott, F. A. & McCreary Jr, J. P. The monsoon circulation of the Indian Ocean. *Prog. Oceanogr.* **51**, 1–123 (2001).

22. Gao, J., Masson-Delmotte, V., Risi, C., He, Y. & Yao, T. What controls precipitation δ¹⁸O in the southern Tibetan Plateau at seasonal and intra-seasonal scales? A case study at Lhasa and Nyalam. *Tellus B* **65**, 21043–21055 (2013).

23. Zhang, L., Su, F., Yang, D., Hao, Z. & Tong, K. Discharge regime and simulation for the upstream of major rivers over Tibetan Plateau. *J. Geophys. Res. D* **118**, 8500–8518 (2013).

24. Immerzeel, W. W., Droogers, P., De Jong, S. M. & Bierkens, M. F. P. Large-scale monitoring of snow cover and runoff simulation in Himalayan river basins using remote sensing. *Remote Sens. Environ.* **113**, 40–49 (2009).

25. Kääb, A., Berthier, E., Nuth, C., Gardelle, J. & Arnaud, Y. Contrasting patterns of early twenty-first-century glacier mass change in the Himalayas. *Nature* **488**, 495–498 (2012).

26. Falkenmark, et al. *On the Verge of a New Water Scarcity: A Call for Good Governance and Human Ingenuity* (Stockholm International Water Institute, 2007).

27. Pritchard, H. D. Asia's shrinking glaciers protect large populations from drought stress. *Nature* **569**, 649–654 (2019).

28. Falkenmark, M. Meeting water requirements of an expanding world population. *Philos. Trans. R. Soc. Lond.* B **352**, 929–936 (1997).

29. Jones, B. & O'Neill, B. C. Spatially explicit global population scenarios consistent with the shared socioeconomic pathways. *Environ. Res. Lett.* **11**, 084003 (2016).

**Publisher's note** Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

© The Author(s), under exclusive licence to Springer Nature Limited 2021
## Methods

**Observations.** We used three monthly precipitation datasets from the Climatic Research Unit Time Series v.4.03 (CRU) (http://www.cru.uea.ac.uk/data), the GPCC (https://www.dwd.de/EN/ourservices/gpcc/gpcc.html) and the University of Delaware monthly gridded precipitation data v.5.01 (UDEL) at 0.5° spatial resolution for the period 1960–2005 (https://www.esrl.noaa.gov/psd/data/gridded/data.UDel_AirT_Precip.html). The gridded SLP data are taken from the Hadley Centre mean SLP dataset v.2 (HadSLP2) (https://psl.noaa.gov/data/gridded/data.hadslp2.html) at a spatial resolution of 5° for the period 1960–2005.

**Climate model simulations.** We used both historical (1960–2005) and future simulations (2006–2099) from ESMs that participated in CMIP5 and CMIP6. For the future projections, we consider ESM simulations that are performed at the three different levels of forcing (RCP 2.6, RCP 4.5 and RCP 8.5) in CMIP5 (ref. 30) and that are forced by integrated scenarios of future climate and societal change (SSPx-y) in CMIP6, where x is the specific SSP and y indicates the RCP 31. In the CMIP5 archive, RCP 2.6 (RCP 4.5 and RCP 8.5) is a pathway where the radiative forcing peaks at 2.6 W m⁻² (4.5 and 8.5 W m⁻²) before the year 2100¹². In the CMIP6 archive, SSPx-y refers to an integrated scenario that combines climate simulations on the basis of the RCP and societal conditions described by any of the SSPs. Here, SSP 1–2.6, SSP 2–4.5 and SSP 5–8.5 were considered.

We extracted monthly mean precipitation (‘pr’), evapotranspiration (converted from the latent heat flux ‘hfls’), SLP (psl) and total runoff (mrro) from all available ESMs having both historical (1960–2005) and future (2006–2099) experiments archived in CMIP5 and CMIP6. In the hydrological submodel of the ESMs, the total runoff leaving the land portion of a grid cell consists of the surface runoff plus the water drainage through the base of its soil module. Note that the hydrological module in most of CMIP models does not include glacier processes and the contribution from glacier melt to the total runoff is missing. The CMIP5 model experiments include several initial states (r), initialization methods (i) and physics (p)¹³ and CMIP6 experiments have an extra index describing the variant of forcing (f). Different models have different numbers of realizations, with some having only one but others having up to more than 50. Given that each realization has its own internal variability, ensemble means of different numbers of realizations might give different ranges of variability across ESMs¹⁴. For this reason, we present results only from the first realization (r1i1p1 for CMIP5 and r1i1p1f1 for CMIP6) for each individual ESM, instead of from the ensemble mean over all available realizations. We averaged modelled SLP data to 5° × 5° and interpolated precipitation data to a spatial resolution of 0.5° using the first-order conservative regridding method.

To obtain local climate responses at 1.5°C, we selected ESMs that have a global (land and ocean) temperature increase between pre-industrial levels (1850–1900) and the end of this century (2070–2099) within the range of 1.3–1.7°C (1.8–2.2 for 2°C and 3.6–4.4°C for 4°C) (Supplementary Table 2). We considered compatible CMIP5 RCPs and CMIP6 SSPx-y scenarios to represent each warming category. For example, on the basis of the ESM ensemble, RCP 2.6 and SSP 1–2.6 have a median warming of 1.43°C and 1.31°C at the end of this century above pre-industrial level, which are generally consistent with the 1.5°C pathway that has the goal of limiting global warming to 1.5°C in 2100. We selected RCP 2.6 and SSP 1–2.6, RCP 4.5 and SSP 2–4.5 and RCP 8.5 and SSP 5–8.5 scenarios to approximate the 1.5, 2 and 4°C pathways, respectively. Specifically, for each model, we first calculated global mean temperature (GMT, including both land and ocean cells) for the pre-industrial period 1850–1900. We then computed the GMT of the end of this century for each ESM under CMIP5 RCPs and CMIP6 SSPs, so as to assess whether the deviation of GMT from its pre-industrial value is within the range of 1.3–1.7°C for 1.5°C (1.8–2.2 for 2°C and 3.6–4.4°C for 4°C). The 1.5°C level thus involves ten ESMs that are originated from the RCP 2.6 (six) and SSP 1–2.6 scenarios (four). The 2°C level includes 17 ESMs from RCP 4.5 (13) and SSP 2–4.5 (four), while the 4°C level involves 11 ESMs from RCP 8.5 (six) and SSP 5–8.5 (five). The wet-season precipitation and runoff that were averaged over this time period were then regarded as local climate change signals that would be anticipated under a given global warming level.

**Debiasing of SLP from model simulations.** We applied the change factor method to correct biases in future SLP at the monthly timescale¹⁵. This debiasing procedure comprised the following steps. First, for each month, we calculated the multi-year means of the historical period (1960–2005) and end of this century (2070–2099) under different CMIP5 RCPs and CMIP6 SSPs from each ESM. Second, we computed anomalies as the relative change in SLP between the historical and future periods.

$$ \Delta \text{SLP}_m = \frac{\text{SLP}_{\text{Fm}} - \text{SLP}_{\text{Hm}}}{\text{SLP}_{\text{Hm}}} \quad (1) $$

where ΔSLP_m is the delta change, SLP_Hm is the multi-year mean SLP in the historical period and SLP_Fm is the multi-year mean of SLP at the end of this century from each ESM in the month m. Third, we averaged the SLP anomalies to a spatial resolution of 5° and then added these anomalies to the corresponding historical observations to get the debiased future values. Here the HadSLP2 (spatial resolution of 5°) is used as the reference observation dataset for SLP in the historical period.

**Disentangling dynamic and thermodynamic contributions of precipitation changes.** We calculated monthly vertically integrated moisture flux convergence as

$$ -\nabla \cdot \mathbf{Q} = -\frac{1}{g_0} \nabla \cdot \int_0^{p_s} q \mathbf{V} \, dp \quad (2) $$

where Q is the vertically integrated water vapour flux, V is wind velocity, q is specific humidity, g_0 and p_s represent the acceleration due to gravity and surface pressure, respectively.

Change in moisture flux convergence, which has a close relation with precipitation change, can be decomposed into a dynamic component, related to atmospheric circulation change and a thermodynamic component, related to temperature-induced change in atmospheric water vapour. The column-integrated moisture flux convergence is decomposed as

$$ -\frac{1}{g_0} \int_0^{p_s} \nabla \cdot (q\mathbf{V}) dp \approx -\frac{1}{g_0} \int_0^{p_s} \nabla \cdot (q\bar{\mathbf{V}}) dp - \frac{1}{g_0} \int_0^{p_s} \nabla \cdot (\bar{q}\mathbf{V}) dp \quad (3) $$

where overbars indicate wet-season (June to September) climatology during 1960–2005 and primes represent the deviation from climatology. $-\int_0^{p_s} \nabla \cdot (q\bar{\mathbf{V}}) dp$ provides a metric of the thermodynamic process, with the enhancement of atmospheric water vapour content following the Clausius–Clapeyron equation. $-\int_0^{p_s} \nabla \cdot (\bar{q}\mathbf{V}) dp$ is the dynamic component, related to changes in atmospheric circulation¹⁶.

**Atmospheric dynamic constraints on future precipitation projections.** We used SLP to indicate large-scale atmospheric circulation, given that the SLP is well constrained in the long reanalyses and representative of the main large-scale atmospheric features. To derive the empirical link between precipitation over the Tibetan Plateau and the large-scale atmospheric circulation in the historical period 1960–2005, we performed a regression analysis between gridded observational datasets of precipitation and SLP over the entire globe. First, we extracted the leading modes of spatiotemporal precipitation variability using a standard empirical orthogonal function analysis, also known as principal components analysis. In this application, the variables were precipitation time series at each of all the grid locations over the Tibetan Plateau, with each time series covering the period 1960–2005. The first ten principal components were retained for further analysis, since the mean wet-season precipitation reconstructed from these components indicated almost no difference with the original one in both the observations and the ESM simulations (Supplementary Fig. 14). Second, we built the empirical multivariate regression model to predict each of the retained principal components from the HadSLP2 SLP data at 5°-resolution grid cells over the globe. To select a subset of potential SLP predictors, we resorted to the least absolute shrinkage and selection operator regression method which can reduce predictors through shrinking the estimates of their regression coefficients towards zero¹⁷. Note that this regression analysis was done separately for each month of the wet season (June, July, August and September) and also for each month of the cold season (January to May). To ensure a robust estimate of regression coefficients, we also included the neighbouring months, which allowed us to increase the record length by a factor of three (46 × 3 months) in the regression analyses. Note also that the predictors were monthly normalized SLP anomalies at a spatial resolution of 5° from the HadSLP2 dataset¹⁸. The predicted principal components are further used to reconstruct the precipitation that can be considered to be mainly driven by the large-scale atmospheric circulation.

Third, for each ESM under the CMIP5 RCP and CMIP6 SSP scenarios, we estimated the precipitation during 2006–2099 from bias-corrected SLP changes simulated by the ESMs, using the established regression model between observed precipitation and SLP during 1960–2005, as described in the above paragraph. When compared to the ESM-simulated changes in precipitation, these data offered an estimate of the improved accuracy and potential uncertainty reduction that could be achieved if ESM biases in the representation of the precipitation–SLP relationship were alleviated. The constraint approach was done separately for each ESM under a particular forcing scenario from either CMIP5 or CMIP6. To obtain constrained precipitation changes at a given global warming level, we then averaged the models that were selected from corresponding CMIP5 RCPs or CMIP6 SSPs (Supplementary Table 2).

**Constraint on future runoff projection.** For each model under each future scenario, we calculated the projected change in mean wet-season precipitation and runoff at the end of this century, with respect to the historical period (expressed as a percentage of mean precipitation and runoff in the historical period), over each upstream region of seven rivers (Indus, Ganges, Brahmaputra, Salween and Mekong, Yangtze and Yellow). For each upstream region, there is a strong linear correlation between runoff changes and precipitation changes across CMIP5 and CMIP6 ESMs. This relationship enables a strong constraint on future runoff changes via observationally constrained precipitation changes. Such observationally constrained precipitation is derived by applying the
empirical precipitation-SLP relationship onto ESM-simulated SLP changes. We also calculated probability density functions of ESM-simulated and precipitation-constrained runoff changes using the same methodology as detailed in Cox et al.¹⁹.

**Future changes in glacier runoff.** Here, we used existing simulated monthly glacier runoff data forced by the three emission scenarios (RCP 2.6, RCP 4.5 and RCP 8.5)⁵. This dataset is created using the Global Glacier Evolution Model (GloGEM), which simulates mass changes and associated geometry changes for all individual glaciers present in Randolph Glacier Inventory v.4.0. The glacier runoff refers to all the water originating from the glacier net mass loss and including the sum of snow melt and rain minus the refreezing over the initially glaciated area. This runoff value also includes all the water originating from ice-free areas, once the initial glaciers have retreated in a warmer future⁵. Therefore, we have to exclude precipitation over the initially glaciated area, as given by Randolph Glacier Inventory, when analysing basin-scale runoff changes.

To approximate glacier runoff changes between the historical period and the end of this century, we used glacier runoff projections from the Huss and Hock study⁵. Specifically, we used the projections made under the RCP 2.6 scenario for 1.5°C warming and those made using the RCP 4.5 and RCP 8.5 scenarios for 2°C and 4°C warming, respectively. To examine the robustness of this approximation, we investigated whether the projected temperature changes between the historical period and the end of this century from the CMIP5 models, which were used in the glacier runoff simulations of Huss and Hock² under RCP 2.6, RCP 4.5 and RCP 8.5, were within our prescribed temperature ranges for 1.5°C, 2°C and 4°C warming. We found that the multimodel mean temperature increase from their selected ESMs under RCP 2.6 was 1.4°C while for RCP 4.5 it was 1.9°C: both values being well within the respective temperature ranges of our ESM selection criteria (1.3–1.7°C for 1.5°C and 1.8–2.2°C for 2°C). While, the multimodel mean temperature increase for RCP 8.5 (3.2°C) fell slightly outside of our range (3.6–4.4°C) for 4°C warming.

**Estimating the percentage of population living above various water scarcity levels.** We used Falkenmark's water crowding indicator³⁰, which is calculated by dividing available water resources by the total population within a geographic area, to measure the degree of water scarcity. Here, the available water resources refer to the runoff summed over all grid cells in the entire upstream basin and the population is integrated over the area that is strongly dependent on upstream water resources (the highly dependent area). For each basin, we first determined the extent of the highly dependent area and then estimated the percentage of the population living in the highly dependent area which could be satisfied by wet-season upstream runoff for a full year at a given threshold of water scarcity (500 m³ or 1,000 m³ per person per year). The highly dependent area includes the entire upstream basin and a certain fraction of the downstream region which shows a relatively high reliance on upstream water resources. To quantify this downstream area, we used the water transit time, defined as the time in which a certain amount of water travels through a river reach, to assess the dependence of downstream grid cells on upstream ones. At each grid cell, the water transit time, $t_w$ (days), is calculated as a function of its area, $A_{region}$ (km²), the mean discharge, $Q(m^3 s^{-1})$ and the main slope, $\beta$ (m km⁻¹) using the following formula⁵.

$$t_w = 0.055 A_{region}^{0.50} \bar{Q}^{-0.10} \beta^{-0.35} \quad (4)$$

The slope at each grid cell is extracted from global digital elevation models. The estimation of the transit time of water flowing from upstream grid cells to a cell within the downstream region involves the two steps. First, we assigned the water flow path for the downstream grid cell, tracing from upstream cells using the eight-direction (D8) method⁴¹. However, this widely used approach for delineating flow paths suffered from substantial problems in grid cells with mountains and glaciers, where a circular flow path, with adjacent cells draining into each other, could be generated. Therefore, before conducting the water flow path analysis, we removed 'peaks' and filled 'sinks' to hydrologically correct the elevation data. In addition, not all of the downstream grid cells in a river basin could trace their flow of water back to cells in the upstream and downstream cells with their own water flow paths that can be traced back to their corresponding upstream cells are retained. In a second step, for each grid cell in the downstream region, the transit time of the water flowing from an upstream cell is then computed by accumulating the transit time through all the previous grid cells along its water flow path. After mapping the spatial distribution of the transit time of wet-season runoff from upstream cells to each cell of the downstream region, we used a transit-time threshold of 10 d to select the downstream cells that denoted the downstream area supposed to be predominantly dependent on upstream water resources. Here, the historical runoff data used to compute grid-scale transit time was daily climatological (1960–2005) discharge at a spatial resolution of 0.25° from the Global Land Data Assimilation System (GLDAS) Noah product.

We should note that the empirical parameters often used to compute water transit time at the basin scale might have uncertainties when used at the grid cell level. For example, the maximum transit time of downstream cells for each river would slightly deviate from that found in previous studies conducted at the whole

basin scale (Supplementary Fig. 15). Here, we used the concept of transit times as inversion tools to quantify the area of the basin which has a predefined transit time, which is contrary to prior studies that computed the transit time from a given area of basin⁵. Our grid-scale inversion approach would be much more efficient than the basin-scale inversion method, in which transit times from all subareas of the basin should be calculated and iterated through to find the area having a transit time of less than 10 d. More importantly, unlike with our grid-scale inversion approach, we would not be able to ensure that the identified area from the basin-scale inversion approach has self-consistent water flow pathways.

The population projections are taken from the SSPs. Here, we linked population projections from different SSPs to different global warming levels, since the effectiveness and costs of mitigation measures taken to achieve different warming levels strongly depend on future socioeconomic developments. The SSPs have five distinct textual descriptions of plausible future changes in demographics, socioeconomic, technology and environment. They include a world of sustainability (SSP 1), a middle-of-the-road world (SSP 2), a regional rivalry world (SSP 3), an inequality world (SSP 4) and a world of fossil-fuelled development (SSP 5)²⁹. The SSPs covered the socioeconomic dimension of different possible futures according to their broad societal narratives and offer us a wide range of possibilities to explore mitigation measures for achieving different global warming levels. Population change, together with economic growth and urbanization, comprise the basic elements of SSPs. Moreover, for each SSP, economic projections are developed together with demographic projections, to ensure an internal consistency with its corresponding narrative. Their projections form quantitative inputs to integrated assessment models for developing integrated scenarios that interpret SSPs in terms of land use change, greenhouse gas emissions and other variables. But not all SSPs could produce scenarios that limit the radiative forcing to 2.6 W m⁻² by 2100³¹, a level close to the 1.5°C target of the Paris climate agreement. Moreover, according to mitigation costs, the probability of producing stringent climate targets under SSPs is high in low mitigation challenges (SSP 1/SSP 4), low in high challenges (SSP 3/SSP 5) and medium in the intermediate challenge (SSP 2)³¹. Therefore, we selected feasible combinations of SSP population projections and global warming levels (1.5°C-SSP 1, 1.5°C-SSP 4, 2°C-SSP 2, 4°C-SSP 3 and 4°C-SSP 5) to quantify the future water stress under each warming level by the end of this century. In addition, the SSPs described population projections by considering age, sex and educational attainment level⁴² and they present a major step forward as compared to other projected population datasets, such as the World Population Prospects that consider only the age- and sex-structure of the population.

## Data availability

The data that support the findings of this study are publicly available. The observational grid precipitations are available from https://www.dwd.de/EN/ourservices/gpcc/gpcc.html, http://www.cru.uea.ac.uk/data and https://www.esrl.noaa.gov/psd/data/gridded/data.UDel_AirT_Precip.html. The SLP dataset is available from https://www.esrl.noaa.gov/psd/gcos_wgsp/Gridded/data.hadslp2.html. The CMIP output is available from the Earth System Grid Federation at https://esgf-node.llnl.gov/projects/cmip5/, https://esgf-node.llnl.gov/search/cmip6/. The population data are available from https://sedac.ciesin.columbia.edu/data/set/popdynamics-1-km-downscaled-pop-base-year-projection-ssp-2000-2100-rev01. The glacier runoff data were extracted from https://www.nature.com/articles/s41558-017-0049-x#Sec14. The GLDAS Noah product is available from https://ldas.gsfc.nasa.gov/gldas/model-output. The Japanese 55-year reanalysis data are available from http://jra.kishou.go.jp/.

## Code availability

The codes used to perform the calculation and produce all figures are available from GitHub at https://github.com/zhaoyutong/.

## References

30. Taylor, K. E., Stouffer, R. J. & Meehl, G. A. An overview of CMIP5 and the experiment design. *Bull. Am. Meteorol. Soc* 93, 485–498 (2012).

31. O'Neill, B. C. et al. The scenario model intercomparison project (ScenarioMIP) for CMIP6. *Geosci. Model Dev.* 9, 3461–3482 (2016).

32. Van Vuuren, D. P. et al. The representative concentration pathways: an overview. *Climatic Change* 109, 5–31 (2011).

33. Hawkins, E. & Sutton, R. The potential to narrow uncertainty in regional climate predictions. *Bull. Am. Meteorol. Soc.* 90, 1095–1107 (2009).

34. Anav, A. et al. Evaluating the land and ocean components of the global carbon cycle in the CMIP5 earth system models. *J. Clim.* 26, 6801–6843 (2013).

35. Navarro, R. C. et al. High-resolution and bias-corrected CMIP5 projections for climate change impact assessments. *Sci. Data* 7, 1–14 (2020).

36. 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).

37. Tibshirani, R. Regression shrinkage and selection via the lasso. *J. R. Stat. Soc. B* 58, 267–288 (1996).
38. Allan, R. & Ansell, T. A new globally complete monthly historical gridded mean sea level pressure dataset (HadSLP2): 1850–2004. *J. Clim.* **19**, 5816–5842 (2006).

39. Cox, P. et al. Sensitivity of tropical carbon to climate change constrained by carbon dioxide variability. *Nature* **494**, 341–344 (2013).

40. O'Callaghan, J. F. & Mark, D. M. The extraction of drainage networks from digital elevation data. *Computer Vision Graphics Image Process.* **28**, 323–344 (1984).

41. Rogelj, J. et al. Energy system transformations for limiting end-of-century warming to below 1.5°C. *Nat. Clim. Change* **5**, 519–527 (2015).

42. Samir, K. C. & Lutz, W. The human core of the shared socioeconomic pathways: population scenarios by age, sex, and level of education for all countries to 2100. *Glob. Environ. Change* **42**, 181–192 (2017).

## Acknowledgements

This study was supported by the Second Tibetan Plateau Scientific Expedition and Research Programme (2019QZKK0208), the NSFC project Basic Science Centre for Tibetan Plateau Earth System (41988101-04), Key Research and Development Programmes for Global Change and Adaptation (2017YFA0603604) and the National Natural Science Foundation of China (41922004 and 41871104). We acknowledge

the support of Kathmandu Centre for Research and Education, Chinese Academy of Sciences–Tribhuvan University.

## Author contributions

T.W. designed the study and wrote the paper. Y.Z. performed the analysis. All authors contributed to the interpretation of the results and to the text.

## Competing interests

The authors declare no competing interests.

## Additional information

*Extended data* is available for this paper at https://doi.org/10.1038/s41558-020-00974-8.

*Supplementary information* is available for this paper at https://doi.org/10.1038/s41558-020-00974-8.

**Correspondence and requests for materials** should be addressed to T.W.

**Peer review information** *Nature Climate Change* thanks Daniel Farinotti, Santosh Nepal and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

**Reprints and permissions information** is available at www.nature.com/reprints.
<figure><img src="image_7.png" /></figure>

**Extended Data Fig. 1** | Simulated wet-season temperature change over the upper river basins sourced from the Tibetan Plateau at different warming levels. The multimodel ensemble mean temperature difference between pre-industrial period (1850-1900) and future periods (2070-2099) at different warming levels over the upper Indus (a), Ganges (b), Brahmaputra (c), Salween and Mekong (d), Yangtze (e) and Yellow river basins (f). The dark grey region denotes the geographical location of each river basin. Blue, yellow and red represent the 1.5, 2 and 4 °C, respectively. Error bars indicate one standard deviation of intermodel spread. The number of CMIP5 and CMIP6 ESMs used to calculate projected temperature change is 6 and 4 for 1.5 °C of global warming (13 and 4 for 2 °C, and 6 and 5 for 4 °C). The wet season here is defined as the period from June to September.
<figure><img src="image_8.png" /></figure>

**Extended Data Fig. 2 | Partitioning of intermodel uncertainty in projected wet-season runoff change over upper river basins sourced from the Tibetan Plateau at the three warming levels.** The multi-circular bar shows the intermodel uncertainty in projected runoff changes that can be partitioned into the individual components of projected changes in precipitation (blue), evapotranspiration (green) and the residual term (for example snow and soil water storage). Contributions are calculated as a percentage (%), estimated by means of analysis of variance. Dark grey, white, orange and light blue represent the Tibetan Plateau, the glaciered region, the upper river basin and ocean, respectively. The wet season here is defined as the period from June to September. The number of CMIP5 and CMIP6 ESMs used to calculate projected temperature change is 6 and 4 for 1.5 °C of global warming (13 and 4 for 2 °C, and 6 and 5 for 4 °C).
<figure><img src="image_9.png" /></figure>

**Extended Data Fig. 3** | Same as Extended Data Fig. 2, except that the uncertainty partitioning is performed using the period from January to September in upper Indus basin. The multi-circular bar shows the intermodel uncertainty in projected runoff changes that can be partitioned into the individual components of projected changes in precipitation (blue), evapotranspiration (green) and the residual term (for example snow and soil water storage). Contributions are calculated as a percentage (%), estimated by means of analysis of variance.
<figure><img src="image_10.png" /></figure>

**Extended Data Fig. 4** | The coefficient of determination between interannual variabilities of precipitation and atmospheric moisture flux components over the Tibetan Plateau from 1960 to 2005 for JRA-55 and ESMs. -∇·Q, Dyn and Thermo represent the moisture flux convergence, the dynamic component and the thermodynamic component of moisture flux convergence, respectively. JRA-55 indicates the Japanese 55-year reanalysis data, and ESM indicates the Earth System Model from CMIP5 and CMIP6. The number of models from CMIP5 and CMIP6 historical experiments are 18 and 11, respectively.
<figure><img src="image_11.png" /></figure>

**Extended Data Fig. 5** | Simulated and statistical-predicted precipitation over Tibetan Plateau under 1.5°C. Wet season (June to September) precipitation averaged over the Tibetan Plateau, simulated by CMIP ESMs (black line) and statistically predicted (red) from 1960 to 2099. All time series are smoothed with a 21-yr running mean filter. *R* indicates the correlation coefficient between simulated and statistical-predicted precipitation.
<figure><img src="image_12.png" /></figure>

**Extended Data Fig. 6** | Simulated and constrained projections of wet-season precipitation changes over the Tibetan Plateau at different warming levels using the historical observations ensemble mean precipitation (GPCC, CRU and UDEL) and SLP relationship as a constraint on model projections of future precipitation changes. The multimodel ensemble mean precipitation difference (in percentage) is calculated between historical period (1960-2005) and future periods (2070-2099) from CMIP original (a, c, and e) and constrained model projections (b, d and f) at different temperature levels. SLP is sea-level pressure. Dots indicates grids where all models agree on the sign of change. The bars indicate the frequency of distribution of the precipitation change. The wet season here is defined as the period from June to September. The number of CMIP5 and CMIP6 ESMs is 6 and 4 for 1.5 °C of global warming (13 and 4 for 2 °C, and 6 and 5 for 4 °C).
<figure><img src="image_13.png" /></figure>

**Extended Data Fig. 7** | Same as Fig. 4, but for projected changes in percentage of population living above chronic water scarcity level (>1000 m3 per person per year) at the three warming levels. The bars denote the percentage of the population living in the highly dependent area that could be satisfied by wet-season upstream runoff for a full year at the chronic water scarcity level (1000 m³ per person per year), and brown points represent the population (in millions) integrated over the highly dependent area.