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.

  • Letter
  • Published:

The future intensification of hourly precipitation extremes


Extreme precipitation intensities have increased in all regions of the Contiguous United States (CONUS)1 and are expected to further increase with warming at scaling rates of about 7% per degree Celsius (ref. 2), suggesting a significant increase of flash flood hazards due to climate change. However, the scaling rates between extreme precipitation and temperature are strongly dependent on the region, temperature3, and moisture availability4, which inhibits simple extrapolation of the scaling rate from past climate data into the future5. Here we study observed and simulated changes in local precipitation extremes over the CONUS by analysing a very high resolution (4 km horizontal grid spacing) current and high-end climate scenario that realistically simulates hourly precipitation extremes. We show that extreme precipitation is increasing with temperature in moist, energy-limited, environments and decreases abruptly in dry, moisture-limited, environments. This novel framework explains the large variability in the observed and modelled scaling rates and helps with understanding the significant frequency and intensity increases in future hourly extreme precipitation events and their interaction with larger scales.

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

Access options

Buy this article

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

Figure 1: Hourly extreme precipitation is increasing in the majority of the domain, while mean and moderate intense precipitation are substantially decreasing in large areas.
Figure 2: Relative changes in the exceedance probability of the control period 99.95th percentile of hourly precipitation intensities.
Figure 3: The scaling rates between daily mean temperature and extreme precipitation (\({\rm P}99_{d_{\max } } \)) are dependent on the available moisture in the atmosphere.
Figure 4: Positive scaling rates are supported if sufficient atmospheric moisture is available, whereas in dry environments negative scaling rates are present.

Similar content being viewed by others


  1. Kunkel, K. E. et al. Monitoring and understanding trends in extreme storms: state of knowledge. Bull. Am. Meteorol. Soc. 94, 499–514 (2013).

    Article  Google Scholar 

  2. Trenberth, K. E., Dai, A., Rasmussen, R. M. & Parsons, D. B. The changing character of precipitation. Bull. Am. Meteorol. Soc. 84, 1205–1217 (2003).

    Article  Google Scholar 

  3. Utsumi, N., Seto, S., Kanae, S., Maeda, E. E. & Oki, T. Does higher surface temperature intensify extreme precipitation? Geophys. Res. Lett. 38, L16708 (2011).

    Article  Google Scholar 

  4. Chan, S. C., Kendon, E. J., Roberts, N. M., Fowler, H. J. & Blenkinsop, S. Downturn in scaling of UK extreme rainfall with temperature for future hottest days. Nat. Geosci. 9, 24–28 (2016).

    Article  CAS  Google Scholar 

  5. Ban, N., Schmidli, J. & Schär, C. Heavy precipitation in a changing climate: does short-term summer precipitation increase faster? Geophys. Res. Lett. 42, 1165–1172 (2015).

    Article  Google Scholar 

  6. Downton, M. W., Miller, J. Z. B. & Pielke, R. A. Jr Reanalysis of US National Weather Service flood loss database. Nat. Hazards Rev. 6, 13–22 (2005).

    Article  Google Scholar 

  7. Lenderink, G. & Van Meijgaard, E. Increase in hourly precipitation extremes beyond expectations from temperature changes. Nat. Geosci. 1, 511–514 (2008).

    Article  CAS  Google Scholar 

  8. Westra, S. et al. Future changes to the intensity and frequency of short-duration extreme rainfall. Rev. Geophys. 52, 522–555 (2014).

    Article  Google Scholar 

  9. Hardwick Jones, R., Westra, S. & Sharma, A. Observed relationships between extreme sub daily precipitation, surface temperature, and relative humidity. Geophys. Res. Lett. 37, L22805 (2010).

    Article  Google Scholar 

  10. Neelin, J. D., Peters, O. & Hales, K. The transition to strong convection. J. Atmos. Sci. 66, 2367–2384 (2009).

    Article  Google Scholar 

  11. Prein, A. F. et al. A review on regional convection-permitting climate modeling: demonstrations, prospects, and challenges. Rev. Geophys. 53, 323–361 (2015).

    Article  Google Scholar 

  12. Kendon, E. J. et al. Do convection-permitting regional climate models improve projections of future precipitation change? Bull. Am. Meteorol. Soc. (2016).

  13. Jones, T. R. & Randall, D. A. Quantifying the limits of convective parameterizations. J. Geophys. Res. 116, D08210 (2011).

    Google Scholar 

  14. Kendon, E. J. et al. Heavier summer downpours with climate change revealed by weather forecast resolution model. Nat. Clim. Change 4, 570–576 (2014).

    Article  Google Scholar 

  15. Liu, C. et al. Continental-scale convection-permitting modeling of the current and future climate of North America. Clim. Dynam. (2016).

  16. Skamarock, W. C. et al. A Description of the Advanced Research WRF version 2 Technical note (NCAR, 2005).

  17. Dee, D. et al. The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q. J. R. Meteorol. Soc. 137, 553–597 (2011).

    Article  Google Scholar 

  18. Rasmussen, R. et al. High-resolution coupled climate runoff simulations of seasonal snowfall over Colorado: a process study of current and warmer climate. J. Clim. 24, 3015–3048 (2011).

    Article  Google Scholar 

  19. 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).

    Article  Google Scholar 

  20. Emori, S. & Brown, S. Dynamic and thermodynamic changes in mean and extreme precipitation under changed climate. Geophys. Res. Lett. 32, L17706 (2005).

    Article  Google Scholar 

  21. Maloney, E. D. et al. North American climate in CMIP5 experiments: part iii: assessment of twenty-first-century projections. J. Clim. 27, 2230–2270 (2014).

    Article  Google Scholar 

  22. Muschinski, T. & Katz, J. Trends in hourly rainfall statistics in the United States under a warming climate. Nat. Clim. Change 3, 577–580 (2013).

    Article  Google Scholar 

  23. Wuebbles, D. et al. CMIP5 climate model analyses: climate extremes in the United States. Bull. Am. Meteorol. Soc. 95, 571–583 (2014).

    Article  Google Scholar 

  24. Frierson, D. M. Robust increases in midlatitude static stability in simulations of global warming. Geophys. Res. Lett. 33, L24816 (2006).

    Article  Google Scholar 

  25. Schär, C. et al. Percentile indices for assessing changes in heavy precipitation events. Climatic Change 137, 201–216 (2016).

    Article  Google Scholar 

  26. Data Documentation for Data Set 3240 (DSI-3240) Hourly Precipitation Data (National Climatic Data Center, 2013).

  27. Daly, C. et al. Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States. Int. J. Climatol. 28, 2031–2064 (2008).

    Article  Google Scholar 

  28. Rienecker, M. M. et al. MERRA: NASA’s modern-era retrospective analysis for research and applications. J. Clim. 24, 3624–3648 (2011).

    Article  Google Scholar 

  29. Hershfield, D. M. Rainfall Frequency Atlas of the United States: For Durations from 30 Minutes to 24 Hours and Return Periods from 1 to 100 Years (Department of Commerce, Weather Bureau, 1963).

    Google Scholar 

  30. von Storch, H., Langenberg, H. & Feser, F. A spectral nudging technique for dynamical downscaling purposes. Monthly Weather Rev. 128, 3664–3673 (2000).

    Article  Google Scholar 

  31. Kröner, N. et al. Separating climate change signals into thermodynamic, lapse-rate and circulation effects: theory and application to the European summer climate. Clim. Dynam. (2016).

  32. Wilks, D. S. Statistical Methods in the Atmospheric Sciences Vol. 100 (Academic, 2011).

    Google Scholar 

  33. Bukovsky, M. Masks for the Bukovsky Regionalization of North America (NCAR, 2012).

  34. Varmaghani, A. An analytical formula for potential water vapor in an atmosphere of constant lapse rate. Terr. Atmos. Ocean Sci. 23, 17–24 (2012).

    Article  Google Scholar 

Download references


NCAR is funded by the National Science Foundation and this work was partially supported by the Research Partnership to Secure Energy for America (RPSEA) and NSF EASM Grant AGS-1048829. We thank the ECMWF and NASA for making available their data sets. Computer resources were provided by the Computational and Information Systems Laboratory (NCAR Community Computing;

Author information

Authors and Affiliations



A.F.P. designed the study, and collected and analysed data. C.L. and K.I. performed and post-processed the climate simulations. All authors contributed to the writing process and gave conceptual advice.

Corresponding author

Correspondence to Andreas F. Prein.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Information

Supplementary Information (PDF 8374 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Prein, A., Rasmussen, R., Ikeda, K. et al. The future intensification of hourly precipitation extremes. Nature Clim Change 7, 48–52 (2017).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


Quick links

Nature Briefing Anthropocene

Sign up for the Nature Briefing: Anthropocene newsletter — what matters in anthropocene research, free to your inbox weekly.

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