Letter | Published:

The future intensification of hourly precipitation extremes

Nature Climate Change volume 7, pages 4852 (2017) | Download Citation


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.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.


  1. 1.

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

  2. 2.

    , , & The changing character of precipitation. Bull. Am. Meteorol. Soc. 84, 1205–1217 (2003).

  3. 3.

    , , , & Does higher surface temperature intensify extreme precipitation? Geophys. Res. Lett. 38, L16708 (2011).

  4. 4.

    , , , & Downturn in scaling of UK extreme rainfall with temperature for future hottest days. Nat. Geosci. 9, 24–28 (2016).

  5. 5.

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

  6. 6.

    , & Reanalysis of US National Weather Service flood loss database. Nat. Hazards Rev. 6, 13–22 (2005).

  7. 7.

    & Increase in hourly precipitation extremes beyond expectations from temperature changes. Nat. Geosci. 1, 511–514 (2008).

  8. 8.

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

  9. 9.

    , & Observed relationships between extreme sub daily precipitation, surface temperature, and relative humidity. Geophys. Res. Lett. 37, L22805 (2010).

  10. 10.

    , & The transition to strong convection. J. Atmos. Sci. 66, 2367–2384 (2009).

  11. 11.

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

  12. 12.

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

  13. 13.

    & Quantifying the limits of convective parameterizations. J. Geophys. Res. 116, D08210 (2011).

  14. 14.

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

  15. 15.

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

  16. 16.

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

  17. 17.

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

  18. 18.

    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).

  19. 19.

    , & An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc. 93, 485–498 (2012).

  20. 20.

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

  21. 21.

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

  22. 22.

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

  23. 23.

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

  24. 24.

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

  25. 25.

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

  26. 26.

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

  27. 27.

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

  28. 28.

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

  29. 29.

    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).

  30. 30.

    , & A spectral nudging technique for dynamical downscaling purposes. Monthly Weather Rev. 128, 3664–3673 (2000).

  31. 31.

    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. 32.

    Statistical Methods in the Atmospheric Sciences Vol. 100 (Academic, 2011).

  33. 33.

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

  34. 34.

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

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; http://n2t.net/ark:/85065/d7wd3xhc).

Author information


  1. National Center for Atmospheric Research (NCAR), 3090 Center Green Drive, Boulder, Colorado 80301, USA

    • Andreas F. Prein
    • , Roy M. Rasmussen
    • , Kyoko Ikeda
    • , Changhai Liu
    • , Martyn P. Clark
    •  & Greg J. Holland


  1. Search for Andreas F. Prein in:

  2. Search for Roy M. Rasmussen in:

  3. Search for Kyoko Ikeda in:

  4. Search for Changhai Liu in:

  5. Search for Martyn P. Clark in:

  6. Search for Greg J. Holland in:


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.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Andreas F. Prein.

Supplementary information

PDF files

  1. 1.

    Supplementary Information

    Supplementary Information

About this article

Publication history






Further reading