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Flash droughts present a new challenge for subseasonal-to-seasonal prediction


Flash droughts are a recently recognized type of extreme event distinguished by sudden onset and rapid intensification of drought conditions with severe impacts. They unfold on subseasonal-to-seasonal timescales (weeks to months), presenting a new challenge for the surge of interest in improving subseasonal-to-seasonal prediction. Here we discuss existing prediction capability for flash droughts and what is needed to establish their predictability. We place them in the context of synoptic to centennial phenomena, consider how they could be incorporated into early warning systems and risk management, and propose two definitions. The growing awareness that flash droughts involve particular processes and severe impacts, and probably a climate change dimension, makes them a compelling frontier for research, monitoring and prediction.

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Fig. 1: Evolution of a flash drought across the US Midwest in 2012.
Fig. 2: US Northern Great Plains flash drought in May 2017.
Fig. 3: The response of evaporative demand and evapotranspiration to feedbacks from drying land.
Fig. 4: Frequency of different drought intensification rates.

Data availability

EDDI is available for the CONUS at and for the globe at Figure 1i is generated from the USDM ( The data analysed in Figs. 2 and 4 are available from The data to generate Fig. 4 are available at

Code availability

Figure 1i is generated from the USDM ( Figures 2 and 4 were generated following the protocol The code to generate Fig. 4 is available at


  1. 1.

    Pulwarty, R. S. & Sivakumar, M. V. K. Information systems in a changing climate: early warnings and drought risk management. Weather Clim. Extrem. 3, 14–21 (2014).

    Article  Google Scholar 

  2. 2.

    Global Assessment Report on Disaster Risk Reduction (UNDRR, 2019).

  3. 3.

    Wilhite, D. A. & Pulwarty, R. S. in Drought and Water Crises: Integrating Science, Management, and Policy (eds Wilhite, D. & Pulwarty, R. S.) Ch. 25 (CRC, 2017).

  4. 4.

    Christensen, J. et al. in Climate Change 2007: The Physical Science Basis (eds Solomon, S. et al.) Ch. 11 (IPCC, Cambridge Univ. Press, 2007).

  5. 5.

    Seneviratne, S. I. et al. in Special Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (eds Field, C. B. et al.) 109–230 (IPCC, Cambridge Univ. Press, 2012).

  6. 6.

    Wilhite, D. A., Sivakumar, M. V. K. & Pulwarty, R. Managing drought risk in a changing climate: the role of national drought policy. Weather Clim. Extrem. 3, 4–13 (2014).

    Article  Google Scholar 

  7. 7.

    Svoboda, M. et al. The Drought Monitor. Bull. Am. Meteorol. Soc. 83, 1181–1190 (2002).

    Article  Google Scholar 

  8. 8.

    Otkin, J. A. et al. Flash droughts: a review and assessment of the challenges imposed by rapid-onset droughts in the United States. Bull. Am. Meteorol. Soc. 99, 911–919 (2018).

    Article  Google Scholar 

  9. 9.

    Robertson, A. W. et al. Improving and promoting subseasonal to seasonal prediction. Bull. Am. Meteorol. Soc. 96, ES49–ES53 (2015).

    Article  Google Scholar 

  10. 10.

    Hoerling, M. P. et al. Is a transition to semipermanent drought conditions imminent in the U.S. Great Plains? J. Clim. 25, 8380–8386 (2012).

    Article  Google Scholar 

  11. 11.

    Namias, J. Anatomy of Great Plains protracted heat waves (especially the 1980 U.S. summer drought). Mon. Weather Rev. 110, 824–838 (1982).

    Article  Google Scholar 

  12. 12.

    Yuan, X., Wang, L. & Wood, E. F. Anthropogenic intensification of southern African flash droughts as exemplified by the 2015/16 season. Bull. Am. Meteorol. Soc. 99, S86–S90 (2018).

    Article  Google Scholar 

  13. 13.

    Yuan, X., Ma, Z., Pan, M. & Shi, C. Microwave remote sensing of short‐term droughts during crop growing seasons. Geophys. Res. Lett. 42, 4394–4401 (2015).

    Article  Google Scholar 

  14. 14.

    Li, Y. et al. Mechanisms and early warning of drought disasters: experimental drought meteorology research over China. Bull. Am. Meteorol. Soc. 100, 673–687 (2019).

    Article  Google Scholar 

  15. 15.

    Nguyen, H. et al. Using evaporative stress index to monitor flash drought in Australia. Environ. Res. Lett. (2019).

  16. 16.

    Ford, T. W. & Labosier, C. F. Meteorological conditions associated with the onset of flash drought in the eastern United States. Agric. Meteorol. 247, 414–423 (2017).

    Article  Google Scholar 

  17. 17.

    Hobbins, M. T., Ramírez, J. A. & Brown, T. C. Trends in pan evaporation and actual evapotranspiration across the conterminous U.S.: paradoxical or complementary? Geophys. Res. Lett. 31, (2004).

  18. 18.

    Ramírez, J. A., Hobbins, M. T. & Brown, T. C. Observational evidence of the complementary relationship in regional evaporation lends strong support for Bouchet’s hypothesis. Geophys. Res. Lett. 32, L15401 (2005).

    Article  Google Scholar 

  19. 19.

    Koster, R. D. et al. Flash drought as captured by reanalysis data: disentangling the contributions of precipitation deficit and excess evapotranspiration. J. Hydrometeorol. (2019).

  20. 20.

    Seneviratne, S. I., Lüthi, D., Litschi, M. & Schär, C. Land–atmosphere coupling and climate change in Europe. Nature 443, 205–209 (2006).

    CAS  Article  Google Scholar 

  21. 21.

    Fischer, E. M., Seneviratne, S. I., Lüthi, D. & Schär, C. Contribution of land–atmosphere coupling to recent European summer heat waves. Geophys. Res. Lett. 34, L06707 (2007).

    Article  Google Scholar 

  22. 22.

    Su, H., Yang, Z.-L., Dickinson, R. E. & Wei, J. Spring soil moisture–precipitation feedback in the Southern Great Plains: how is it related to large-scale atmospheric conditions? Geophys. Res. Lett. 41, 1283–1289 (2014).

    Article  Google Scholar 

  23. 23.

    Hoerling, M. et al. Causes and predictability of the 2012 Great Plains drought. Bull. Am. Meteorol. Soc. 95, 269–282 (2014).

    Article  Google Scholar 

  24. 24.

    Mo, K. C. & Lettenmaier, D. P. Precipitation deficit flash droughts over the United States. J. Hydrometeorol. 17, 1169–1184 (2016).

    Article  Google Scholar 

  25. 25.

    Chiang, F., Mazdiyasni, O. & AghaKouchak, A. Amplified warming of droughts in southern United States in observations and model simulations. Sci. Adv. 4, eaat2380 (2018).

    Article  Google Scholar 

  26. 26.

    Pegion, K. et al. The Subseasonal Experiment (SubX): a multi-model subseasonal prediction experiment. Bull. Am. Meteorol. Soc. (2019).

  27. 27.

    Chen, L. G. et al. Flash drought characteristics based on U.S. Drought Monitor. Atmosphere (Basel) 10, 498 (2019).

    Article  Google Scholar 

  28. 28.

    Dirmeyer, P. A., Gentine, P., Ek, M. B. & Balsamo, G. Sub-Seasonal to Seasonal Prediction: The Gap Between Weather and Climate Forecasting (Robertson, A. W. & Vitart, F.) 165–181 (Elsevier, 2019).

  29. 29.

    Waliser, D. E. et al. Potential predictability of the Madden–Julian oscillation. Bull. Am. Meteorol. Soc. 84, 33–50 (2003).

    Article  Google Scholar 

  30. 30.

    Hendon, H. H. et al. Australian rainfall and surface temperature variations associated with the Southern Hemisphere annular mode. J. Clim. 20, 2452–2467 (2007).

    Article  Google Scholar 

  31. 31.

    Zhao, M. & Hendon, H. H. Representation and prediction of the Indian Ocean dipole in the POAMA seasonal forecast model. Q. J. R. Meteorol. Soc. 135, 337–352 (2009).

    Article  Google Scholar 

  32. 32.

    Next Generation Earth System Prediction: Strategies for Subseasonal to Seasonal Forecasts (National Academies Press, 2016).

  33. 33.

    Zhu, H. et al. Seamless precipitation prediction skill in the tropics and extratropics from a global model. Mon. Weather Rev. 142, 1556–1569 (2014).

    Article  Google Scholar 

  34. 34.

    Wheeler, M. C., Zhu, H., Sobel, A. H., Hudson, D. & Vitart, F. Seamless precipitation prediction skill comparison between two global models. Q. J. R. Meteorol. Soc. 143, 374–383 (2017).

    Article  Google Scholar 

  35. 35.

    Wang, L. & Robertson, A. W. Week 3–4 predictability over the United States assessed from two operational ensemble prediction systems. Clim. Dyn. 52, 5861–5875 (2019).

    Article  Google Scholar 

  36. 36.

    Hudson, D. et al. Forewarned is forearmed: extended-range forecast guidance of recent extreme heat events in Australia. Weather Forecast. 31, 697–711 (2016).

    Article  Google Scholar 

  37. 37.

    Vitart, F. & Robertson, A. W. The sub-seasonal to seasonal prediction project (S2S) and the prediction of extreme events. npj Clim. Atmos. Sci. 1, 3 (2018).

    Article  Google Scholar 

  38. 38.

    Lehner, F. et al. Mitigating the impacts of climate nonstationarity on seasonal streamflow predictability in the U.S. Southwest. Geophys. Res. Lett. 44, 12208–12217 (2017).

    Article  Google Scholar 

  39. 39.

    McEvoy, D. J. et al. The Evaporative Demand Drought Index. Part II: CONUS-wide assessment against common drought indicators. J. Hydrometeorol. 17, 1763–1779 (2016).

    Article  Google Scholar 

  40. 40.

    Shukla, S. et al. Examining the value of global seasonal reference evapotranspiration forecasts to support FEWS NET’s food insecurity outlooks. J. Appl. Meteorol. Climatol. 56, 2941–2949 (2017).

    Article  Google Scholar 

  41. 41.

    Zhang, C. et al. CAUSES: diagnosis of the summertime warm bias in CMIP5 climate models at the ARM southern Great Plains site. J. Geophys. Res. Atmos. 123, 2968–2992 (2018).

    Article  Google Scholar 

  42. 42.

    Vitart, F. Madden–Julian Oscillation prediction and teleconnections in the S2S database. Q. J. R. Meteorol. Soc. 143, 2210–2220 (2017).

    Article  Google Scholar 

  43. 43.

    Ukkola, A. M. et al. Land surface models systematically overestimate the intensity, duration and magnitude of seasonal-scale evaporative droughts. Environ. Res. Lett. 11, 104012 (2016).

    Article  Google Scholar 

  44. 44.

    Vitart, F. et al. The Subseasonal to Seasonal (S2S) Prediction Project Database. Bull. Am. Meteorol. Soc. 98, 163–173 (2017).

    Article  Google Scholar 

  45. 45.

    Koster, R. D. et al. Contribution of land surface initialization to subseasonal forecast skill: first results from a multi-model experiment. Geophys. Res. Lett. 37, (2010).

  46. 46.

    Fisher, R. A. et al. Vegetation demographics in Earth System Models: a review of progress and priorities. Glob. Change Biol. 24, 35–54 (2018).

    Article  Google Scholar 

  47. 47.

    Mo, K. C. & Lettenmaier, D. P. Heat wave flash droughts in decline. Geophys. Res. Lett. 42, 2823–2829 (2015).

    Article  Google Scholar 

  48. 48.

    Wang, L., Yuan, X., Xie, Z., Wu, P. & Li, Y. Increasing flash droughts over China during the recent global warming hiatus. Sci. Rep. 6, 30571 (2016).

    CAS  Article  Google Scholar 

  49. 49.

    Zhang, Y., You, Q., Chen, C. & Li, X. Flash droughts in a typical humid and subtropical basin: a case study in the Gan River Basin, China. J. Hydrol. 551, 162–176 (2017).

    Article  Google Scholar 

  50. 50.

    Barnett, T. P. et al. Human-induced changes in the hydrology of the Western United States. Science 319, 1080–1083 (2008).

    CAS  Article  Google Scholar 

  51. 51.

    Marvel, K. et al. Twentieth-century hydroclimate changes consistent with human influence. Nature 569, 59–65 (2019).

    CAS  Article  Google Scholar 

  52. 52.

    Cook, B. I., Mankin, J. S. & Anchukaitis, K. J. Climate change and drought: from past to future. Curr. Clim. Change Rep. 4, 164–179 (2018).

    Article  Google Scholar 

  53. 53.

    Seager, R. et al. Model projections of an imminent transition to a more arid climate in southwestern North America. Science 316, 1181–1184 (2007).

    CAS  Article  Google Scholar 

  54. 54.

    Zhou, S. et al. Land–atmosphere feedbacks exacerbate concurrent soil drought and atmospheric aridity. Proc. Natl Acad. Sci. USA 116, 18848–18853 (2019).

    CAS  Article  Google Scholar 

  55. 55.

    Roderick, M. L., Greve, P. & Farquhar, G. D. On the assessment of aridity with changes in atmospheric CO2. Water Resour. Res. 51, 5450–5463 (2015).

    CAS  Article  Google Scholar 

  56. 56.

    Milly, P. C. D. & Dunne, K. A. Potential evapotranspiration and continental drying. Nat. Clim. Change 6, 946–949 (2016).

    Article  Google Scholar 

  57. 57.

    Feng, S. et al. Why do different drought indices show distinct future drought risk outcomes in the U.S. Great Plains? J. Clim. 30, 265–278 (2017).

    Article  Google Scholar 

  58. 58.

    Lehner, F. et al. Projected drought risk in 1.5 °C and 2 °C warmer climates. Geophys. Res. Lett. 44, 7419–7428 (2017).

    Article  Google Scholar 

  59. 59.

    Swann, A. L. S., Hoffman, F. M., Koven, C. D. & Randerson, J. T. Plant responses to increasing CO2 reduce estimates of climate impacts on drought severity. Proc. Natl Acad. Sci. USA 113, 10019–10024 (2016).

    CAS  Article  Google Scholar 

  60. 60.

    Bonfils, C. et al. Competing influences of anthropogenic warming, ENSO, and plant physiology on future terrestrial aridity. J. Clim. 30, 6883–6904 (2017).

    Article  Google Scholar 

  61. 61.

    Yang, Y., Roderick, M. L., Zhang, S., McVicar, T. R. & Donohue, R. J. Hydrologic implications of vegetation response to elevated CO2 in climate projections. Nat. Clim. Change 9, 44–48 (2019).

    Article  CAS  Google Scholar 

  62. 62.

    Mankin, J. S., Seager, R., Smerdon, J. E., Cook, B. I. & Williams, A. P. Mid-latitude freshwater availability reduced by projected vegetation responses to climate change. Nat. Geosci. (2019).

  63. 63.

    Dirmeyer, P. A. et al. Evidence for enhanced land–atmosphere feedback in a warming climate. J. Hydrometeorol. 13, 981–995 (2012).

    Article  Google Scholar 

  64. 64.

    Otkin, J. A. et al. Assessing the evolution of soil moisture and vegetation conditions during the 2012 United States flash drought. Agric. Meteorol. 218–219, 230–242 (2016).

    Article  Google Scholar 

  65. 65.

    Meko, D. M. et al. Medieval drought in the upper Colorado River Basin. Geophys. Res. Lett. 34, L10705 (2007).

    Article  Google Scholar 

  66. 66.

    Woodhouse, C. A., Meko, D. M., MacDonald, G. M., Stahle, D. W. & Cook, E. R. A 1,200-year perspective of 21st century drought in southwestern North America. Proc. Natl Acad. Sci. USA 107, 21283–21288 (2010).

    CAS  Article  Google Scholar 

  67. 67.

    Woodhouse, C., Stahle, D. & Villanueva Díaz, J. Rio Grande and Rio Conchos water supply variability over the past 500 years. Clim. Res. 51, 147–158 (2012).

    Article  Google Scholar 

  68. 68.

    Woodhouse, C. A. & Pederson, G. T. Investigating runoff efficiency in Upper Colorado River streamflow over past centuries. Water Resour. Res. 54, 286–300 (2018).

    Article  Google Scholar 

  69. 69.

    Lehner, F., Wahl, E. R., Wood, A. W., Blatchford, D. B. & Llewellyn, D. Assessing recent declines in Upper Rio Grande runoff efficiency from a paleoclimate perspective. Geophys. Res. Lett. 44, 4124–4133 (2017).

    Article  Google Scholar 

  70. 70.

    Zhao, M. et al. Weakened eastern Pacific El Niño predictability in the early twenty-first century. J. Clim. 29, 6805–6822 (2016).

    Article  Google Scholar 

  71. 71.

    Huning, L. S. & AghaKouchak, A. Mountain snowpack response to different levels of warming. Proc. Natl Acad. Sci. USA 115, 10932–10937 (2018).

    CAS  Article  Google Scholar 

  72. 72.

    Harpold, A., Dettinger, M. & Rajagopal, S. Defining snow drought and why it matters. Eos (2017).

  73. 73.

    Hoell, A., Perlwitz, J. & Eischeid, J. Drought Assessment Report: The Causes, Predictability, and Historical Context of the 2017 US Northern Great Plains Drought (NOAA/NIDIS/CIRES, 2019).

  74. 74.

    Pulwarty, R. S. & Verdin, J. P. in Measuring Vulnerability to Natural Hazards: Towards Disaster Resilient Societies 2nd edn (ed. Birkmann, J.) 124–147 (United Nations Univ. Press, 2013).

  75. 75.

    Cutter, S. et al. in Special Report on Managing the Risks of Extremes and Disaster to Advance Climate Change Adaptation (eds Field, C. B. et al.) 291–338 (IPCC, Cambridge Univ. Press, 2012).

  76. 76.

    Shrader-Frechette, K. S. Environmental Justice: Creating Equality, Reclaiming Democracy. Environmental Ethics and Science Policy (Oxford Univ. Press, 2002).

  77. 77.

    Jamieson, D. Ethics and the Environment: An Introduction (Cambridge Univ. Press, 2008).

  78. 78.

    Pulwarty, R. S. et al. in Mapping Vulnerability: Disasters, Development and People (eds Bankoff, G. & Frerks, G.) Ch. 6 (Routledge, 2004).

  79. 79.

    Allis, E. et al. The future of climate services. World Meteorol. Organ. Bull. 68, (2019).

  80. 80.

    Gay-Antaki, M. & Liverman, D. Climate for women in climate science: women scientists and the Intergovernmental Panel on Climate Change. Proc. Natl Acad. Sci. USA (2018).

  81. 81.

    Kirtman, B. P. et al. The North American Multimodel Ensemble: Phase-1 seasonal-to-interannual prediction; Phase-2 toward developing intraseasonal prediction. Bull. Am. Meteorol. Soc. 95, 585–601 (2014).

    Article  Google Scholar 

  82. 82.

    Alfieri, L. et al. GloFAS—global ensemble streamflow forecasting and flood early warning. Hydrol. Earth Syst. Sci. 17, 1161–1175 (2013).

    Article  Google Scholar 

  83. 83.

    Arheimer, B. et al. Global catchment modelling using World-Wide HYPE (WWH), open data and stepwise parameter estimation. Hydrol. Earth Syst. Sci. Discuss. (2019).

  84. 84.

    Yuan, X. et al. Anthropogenic shift towards higher risk of flash drought over China. Nat. Commun. 10, 4661 (2019).

    Article  CAS  Google Scholar 

  85. 85.

    Hobbins, M. T., McEvoy, D. J. & Hain, C. R. in Drought and Water Crises: Integrating Science, Management, and Policy (eds Wilhite, D. A. & Pulwarty, R. S.) Ch. 11 (CRC, 2017).

  86. 86.

    Liang, X., Lettenmaier, D. P., Wood, E. F. & Burges, S. J. A simple hydrologically based model of land surface water and energy fluxes for general circulation models. J. Geophys. Res. 99, 14415 (1994).

    Article  Google Scholar 

  87. 87.

    Livneh, B. et al. A long-term hydrologically based dataset of land surface fluxes and states for the conterminous United States: update and extensions. J. Clim. 26, 9384–9392 (2013).

    Article  Google Scholar 

  88. 88.

    Livneh, B. et al. A spatially comprehensive, hydrometeorological data set for Mexico, the U.S., and Southern Canada 1950-2013. Sci. Data 2, 150042 (2015).

    Article  Google Scholar 

  89. 89.

    Lukas, J., Hobbins, M. T., Rangwala, I. & EDDI Team. The EDDI User Guide (NOAA, 2017);

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The perspectives in this manuscript emerged from an Aspen Global Change Institute (AGCI) workshop in September 2018; we thank all participants ( This material is based on work supported by the National Center for Atmospheric Research, which is a major facility sponsored by the National Science Foundation (NSF) under Cooperative Agreement no. 1947282. Portions of this study were supported by the Regional and Global Model Analysis (RGMA) component of the Earth and Environmental System Modeling Program of the US Department of Energy’s Office of Biological & Environmental Research (BER) via NSF IA 1844590. M.H. was supported by a National Oceanic and Atmospheric Administration (NOAA) Joint Technology Transfer Initiative (JTTI) award and a US Agency for International Development (USAID)–Famine Early Warning Systems Network (FEWS NET) award (NA17OAR4320101). C.J.W.B. was supported by an Early-Mid Career LLNL Laboratory Directed Research and Development award (tracking code 17-ERD-115) under the auspices of the US Department of Energy by Lawrence Livermore National Laboratory under contract DE-AC52-07NA27344. A.J.E.G is supported by Australian Research Council Discovery Early Career Researcher Award DE150101297. M.C.W. was partially supported by the Northern Australia Climate Program (NACP), funded by Meat and Livestock Australia, the Queensland Government and the University of Southern Queensland. F.L. is also supported by NSF AGS-0856145 Amendment 87 and by the Bureau of Reclamation under Cooperative Agreement R16AC00039. The US Drought Monitor is jointly produced by the National Drought Mitigation Center at the University of Nebraska-Lincoln, the United States Department of Agriculture, and NOAA. Map (Fig. 1i) courtesy of NDMC. Opinions expressed by D.L. represent professional opinions of the co-author, not official positions of the government.

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A.G.P., G.A.M. and R.P. coordinated the text and organized the workshop. M.H. and A.H. provided figures. All authors except A.A. participated in discussions at the workshop, and all authors contributed to the writing.

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Correspondence to Angeline G. Pendergrass.

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Peer review information Nature Climate Change thanks Laura Ferranti and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Pendergrass, A.G., Meehl, G.A., Pulwarty, R. et al. Flash droughts present a new challenge for subseasonal-to-seasonal prediction. Nat. Clim. Chang. 10, 191–199 (2020).

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