Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Extreme climate events increase risk of global food insecurity and adaptation needs


Climate change is expected to increase the frequency, intensity and spatial extent of extreme climate events, and thus is a key concern for food production. However, food insecurity is usually analysed under a mean climate change state. Here we combine crop modelling and climate scenarios to estimate the effects of extreme climate events on future food insecurity. Relative to median-level climate change, we find that an additional 20–36% and 11–33% population may face hunger by 2050 under a once-per-100-yr extreme climate event under high and low emission scenarios, respectively. In some affected regions, such as South Asia, the amount of food required to offset such an effect is triple the region’s current food reserves. Better-targeted food reserves and other adaptation measures could help fill the consumption gap in the face of extreme climate variability.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Prices vary by article type



Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Probability distributions of per capita food consumption and risk of hunger under two climate pathways.
Fig. 2: Population at risk of hunger.
Fig. 3: Regional probability distribution of crop yields, agricultural price, per capita food consumption and the risk of hunger.
Fig. 4: Food requirements under extreme climate change.

Data availability

The data used in the study are available at the Harvard Dataverse Repository:

Code availability

The code used in the study is available at the Harvard Dataverse Repository:


  1. Handmer, J. et al. in Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation, (eds Field, C. B. et al.) 231–290 (Cambridge Univ. Press, 2012).

  2. Tao, F. & Zhang, Z. Climate change, high-temperature stress, rice productivity, and water use in eastern China: a new superensemble-based probabilistic projection. J. Appl. Meteorol. Climatol. 52, 531–551 (2013).

    Article  ADS  Google Scholar 

  3. Challinor, A. J., Simelton, E. S., Fraser, E. D. G., Hemming, D. & Collins, M. Increased crop failure due to climate change: assessing adaptation options using models and socio-economic data for wheat in China. Environ. Res. Lett. 5, 034012 (2010).

    Article  ADS  Google Scholar 

  4. Urban, D., Roberts, M. J., Schlenker, W. & Lobell, D. B. Projected temperature changes indicate significant increase in interannual variability of US maize yields. Clim. Change 112, 525–533 (2012).

    Article  ADS  Google Scholar 

  5. Müller, C. & Robertson, R. D. Projecting future crop productivity for global economic modeling. Agric. Econ. 45, 37–50 (2014).

    Article  Google Scholar 

  6. Nelson, G. C. et al. Climate change effects on agriculture: economic responses to biophysical shocks. Proc. Natl Acad. Sci. USA 111, 3274–3279 (2014).

    Article  ADS  CAS  PubMed  Google Scholar 

  7. Rosenzweig, C. & Parry, M. L. Potential impact of climate change on world food supply. Nature 367, 133–138 (1994).

    Article  ADS  Google Scholar 

  8. Fischer, G., Shah, M., N. Tubiello, F. & van Velhuizen, H. Socio-economic and climate change impacts on agriculture: an integrated assessment, 1990–2080. Philos. Trans. R. Soc. B Biol. Sci. 360, 2067–2083 (2005).

    Article  Google Scholar 

  9. Nelson, G. C. et al. Food Security, Farming, and Climate Change to 2050, Scenarios, Results, Policy Options (IFPRI, 2010).

    Google Scholar 

  10. Hasegawa, T. et al. Climate Change impact and adaptation assessment on food consumption utilizing a new scenario framework. Environ. Sci. Technol. 48, 438–445 (2014).

    Article  ADS  CAS  PubMed  Google Scholar 

  11. Stevanovic, M. et al. The impact of high-end climate change on agricultural welfare. Sci. Adv. 2, e1501452 (2016).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  12. Lobell, D. B. et al. Prioritizing climate change adaptation needs for food security in 2030. Science 319, 607–610 (2008).

    Article  CAS  PubMed  Google Scholar 

  13. Fuss, S. et al. Global food security & adaptation under crop yield volatility. Technol. Forecast. Soc. Change 98, 223–233 (2015).

    Article  Google Scholar 

  14. Diffenbaugh, N. S., Hertel, T. W., Scherer, M. & Verma, M. Response of corn markets to climate volatility under alternative energy futures. Nat. Clim. Chang. 2, 514–518 (2012).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  15. Ahmed, A. S., Diffenbaugh, S. N. & Hertel, W. T. Climate volatility deepens poverty vulnerability in developing countries. Environ. Res. Lett. 4, 034004 (2009).

    Article  ADS  Google Scholar 

  16. Ahmed, S. A. et al. Climate volatility and poverty vulnerability in Tanzania. Glob. Environ. Change 21, 46–55 (2011).

    Article  Google Scholar 

  17. Suweis, S., Carr, J. A., Maritan, A., Rinaldo, A. & D’Odorico, P. Resilience and reactivity of global food security. Proc. Natl Acad. Sci. USA 112, 6902–6907 (2015).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  18. Puma, M. J., Bose, S., Chon, S. Y. & Cook, B. I. Assessing the evolving fragility of the global food system. Environ. Res. Lett. 10, 024007 (2015).

    Article  ADS  Google Scholar 

  19. Chatzopoulos, T., Perez Dominguez, I., Zampieri, M. & Toreti, A. Climate extremes and agricultural commodity markets: a global economic analysis of regionally simulated events. Weather Clim. Extrem. 27, 100193 (2019).

    Article  Google Scholar 

  20. Katz, R. W. & Brown, B. G. Extreme events in a changing climate: variability is more important than averages. Clim. Change 21, 289–302 (1992).

    Article  ADS  Google Scholar 

  21. Salinger, M. J. Climate variability and change: past, present and future–an overview. Clim. Change 70, 9–29 (2005).

    Article  ADS  CAS  Google Scholar 

  22. Flato, G. et al. in Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.), 741–866 (Cambridge University Press, 2013).

  23. The State of Food Insecurity in the World 2012: Economic Growth Is Necessary but Not Sufficient to Accelerate Reduction of Hunger and Malnutrition (Food and Agriculture Organization, 2012).

  24. Hasegawa, T. et al. Consequence of climate mitigation on the risk of hunger. Environ. Sci. Technol. 49, 7245–7253 (2015).

    Article  ADS  CAS  PubMed  Google Scholar 

  25. Sakurai, G., Iizumi, T., Nishimori, M. & Yokozawa, M. How much has the increase in atmospheric CO2 directly affected past soybean production? Sci. Rep. 4, 4978 (2014).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  26. Müller, C. et al. The global gridded crop model intercomparison phase 1 simulation dataset. Sci. Data 6, 50 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  27. Fujimori, S., Masui, T. and Matsuoka, Y. AIM/CGE [Basic] Manual (Center for Social and Environmental Systems Research, NIES, 2012).

  28. Sillmann, J. et al. Understanding, modeling and predicting weather and climate extremes: challenges and opportunities. Weather Clim. Extrem. 18, 65–74 (2017).

    Article  Google Scholar 

  29. Attribution of Extreme Weather Events in the Context of Climate Change (National Academies Press, 2016).

  30. Stephenson, D. B. in Climate Extremes and Society (eds Diaz H. F. & Murnane R. J.) 11–23 (Cambridge University Press, 2008).

  31. Hasegawa, T., Fujimori, S., Takahashi, K. & Masui, T. Scenarios for the risk of hunger in the twenty-first century using shared socioeconomic pathways. Environ. Res. Lett. 10, 014010 (2015).

    Article  ADS  Google Scholar 

  32. Fujimori, S. et al. A multi-model assessment of food security implications of climate change mitigation. Nat. Sustain. 2, 386–396 (2019).

    Article  Google Scholar 

  33. van Meijl, H., Tabeau, A., Stehfest, E., Doelman, J. & Lucas, P. How food secure are the green, rocky and middle roads: food security effects in different world development paths. Environ. Res. Commun. 2, 031002 (2020).

    Article  Google Scholar 

  34. van Vuuren, D. P. et al. The representative concentration pathways: an overview. Clim. Change 109, 5–31 (2011).

    Article  ADS  Google Scholar 

  35. Hasegawa, T. et al. Risk of increased food insecurity under stringent global climate change mitigation policy. Nat. Clim. Chang. 8, 699–703 (2018).

    Article  ADS  Google Scholar 

  36. Lassa, J. A., Teng, P., Caballero-Anthony, M. & Shrestha, M. Revisiting emergency food reserve policy and practice under disaster and extreme climate events. Int. J. Disaster Risk Sci. 10, 1–13 (2019).

    Article  Google Scholar 

  37. Janssens, C. et al. Global hunger and climate change adaptation through international trade. Nat. Clim. Chang. 10, 829–835 (2020).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  38. International Assessment of Agricultural Knowledge: Science and Technology for Development Global Report (IAASTD, 2009).

  39. Stathers, T., Lamboll, R. & Mvumi, B. M. Postharvest agriculture in changing climates: its importance to African smallholder farmers. Food Sec. 5, 361–392 (2013).

    Article  Google Scholar 

  40. Chriest, A. & Niles, M. The role of community social capital for food security following an extreme weather event. J. Rural Stud. 64, 80–90 (2018).

    Article  Google Scholar 

  41. World Agricultural Supply and Demand Estimates Report (US Department of Agriculture, 2016).

  42. O’Neill, B. C. et al. A new scenario framework for climate change research: the concept of shared socioeconomic pathways. Clim. Change 122, 387–400 (2014).

    Article  ADS  Google Scholar 

  43. Riahi, K. et al. The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: an overview. Global Environ. Change 42, 153–168 (2017).

    Article  Google Scholar 

  44. Fujimori, S. et al. SSP3: AIM implementation of Shared Socioeconomic Pathways. Global Environ. Change 42, 268–283 (2017).

    Article  Google Scholar 

  45. Masutomi, Y., Takahashi, K., Harasawa, H. & Matsuoka, Y. Impact assessment of climate change on rice production in Asia in comprehensive consideration of process/parameter uncertainty in general circulation models. Agric. Ecosyst. Environ. 131, 281–291 (2009).

    Article  Google Scholar 

  46. Denman, K. L. et al. Couplings Between Changes in the Climate System and Biogeochemistry (Cambridge University Press, 2007).

    Google Scholar 

  47. Lal, P. N. et al. in Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation, [Field, C.B. et al. (eds.)]. 339–392 (Cambridge University Press, 2012).

  48. Hertel, T. W. Food security under climate change. Nat. Clim. Chang. 6, 10–13 (2016).

    Article  ADS  Google Scholar 

  49. O’Neill, B. C. et al. Achievements and needs for the climate change scenario framework. Nat. Clim. Chang. 10, 1074–1084 (2020).

    Article  ADS  Google Scholar 

  50. Adoption of the Paris Agreement FCCC/CP/2015/L.9/Rev.1 (UNFCCC, 2015)

  51. Taylor, K. E., Stouffer, R. J. & Meehl, G. A. An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc. 93, 485–498 (2011).

    Article  ADS  Google Scholar 

  52. Hempel, S.F., Frieler, K., Warszawski, L., Schewe, J. & Piontek, F. Bias Corrected GCM Input Data for ISIMIP Fast Track (GFZ Data Services, 2013).

  53. Iizumi, T., Takikawa, H., Hirabayashi, Y., Hanasaki, N. & Nishimori, M. Contributions of different bias-correction methods and reference meteorological forcing data sets to uncertainty in projected temperature and precipitation extremes. J. Geophys. Res. Atmos. 122, 7800–7819 (2017).

    Article  ADS  Google Scholar 

  54. Iizumi, T. et al. Prediction of seasonal climate-induced variations in global food production. Nat. Clim. Chang. 3, 904–908 (2013).

    Article  ADS  Google Scholar 

  55. Parry, M., Rosenzweig, C., Iglesias, A., Fischer, G. & Livermore, M. Climate change and world food security: a new assessment. Global Environ. Change 9, S51–S67 (1999).

    Article  Google Scholar 

  56. Mastrandrea, M. D. et al. Guidance Note for Lead Authors of the IPCC Fifth Assessment Report on Consistent Treatment of Uncertainties (IPCC, 2010).

  57. Zhou, D., Yu, X. & Herzfeld, T. Dynamic Food Demand in Urban China. GlobalFood Discussion Paper (Georg-August-Universität Göttingen, 2014).

  58. Bhargava, A. Estimating short and long run income elasticities of foods and nutrients for rural south India. J. R. Stat. Soc. Ser. A Stat. Soc. 154, 157–174 (1991).

    Article  Google Scholar 

  59. Farquhar, G. D., von Caemmerer, S. & Berry, J. A. A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species. Planta 149, 78–90 (1980).

    Article  CAS  PubMed  Google Scholar 

  60. Neitsch, S. L., Arnold, J. G., Kiniry, J. R., Williams, J. R. & King, K. W. Soil and Water Assessment Tool Theoretical Documentation (Grassland Soil and Water Research Laboratory, Agricultural Research Service, United States Department of Agriculture, 2009).

  61. Iizumi, T. et al. Historical changes in global yields: major cereal and legume crops from 1982 to 2006. Glob. Ecol. Biogeogr. 23, 346–357 (2014).

    Article  Google Scholar 

  62. Vrugt J. A. A. H., et al. Accelerating Markov chain Monte Carlo simulation by differential evolution with self-adaptive randomized subspace sampling. Int. J. Nonlinear Sci. Numer. Simul. 10 (2009).

  63. Baldocchi, D. An analytical solution for coupled leaf photosynthesis and stomatal conductance models. Tree Physiol. 14, 1069–1079 (1994).

    Article  PubMed  Google Scholar 

  64. Fujimori, S., Hasegawa, T., Masui, T. & Takahashi, K. Land use representation in a global CGE model for long-term simulation: CET vs. logit functions. Food Sec. 6, 685–699 (2014).

    Article  Google Scholar 

  65. von Lampe, M. et al. Why do global long-term scenarios for agriculture differ? An overview of the AgMIP global economic model intercomparison. Agric. Econ. 45, 3–20 (2014).

    Article  Google Scholar 

  66. Hanasaki, N. et al. A global water scarcity assessment under Shared Socio-economic Pathways—part 1: water use. Hydrol. Earth Syst. Sci. 17, 2375–2391 (2013).

    Article  ADS  Google Scholar 

  67. FAO Methodology for the Measurement of Food Deprivation: Updating the Minimum Dietary Energy Requirements (Food and Agriculture Organization, 2008).

Download references


This work was supported by the Environment Research and Technology Development Fund (JPMEERF20202002, JPMEERF20211001 and JPMEERF20182001) of the Environmental Restoration and Conservation Agency of Japan, Sumitomo Foundation and the Ritsumeikan Global Innovation Research Organization (R-GIRO), Ritsumeikan University.

Author information

Authors and Affiliations



T.H., G.S., S.F., K.T. and T.M. designed the research. T.H. created figures and wrote the draft of the paper. G.S. performed the crop model experiments. T.H. and S.F. performed the economic model experiments and analysed the data. All authors discussed the results. T.H., G.S., S.F., K.T. and Y.H. contributed to writing the paper.

Corresponding author

Correspondence to Tomoko Hasegawa.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Food thanks the anonymous reviewers for their contribution to the peer review of this work.

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

Supplementary information

Supplementary Information

Supplementary Notes 1–10, Figs. 1–17, Tables 1–5 and References.

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hasegawa, T., Sakurai, G., Fujimori, S. et al. Extreme climate events increase risk of global food insecurity and adaptation needs. Nat Food 2, 587–595 (2021).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing