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Hot extremes have become drier in the United States Southwest


The impacts of summer heat extremes are mediated by humidity. Increases in temperatures due to human-caused climate change are generally expected to increase specific humidity; however, it remains unclear how humidity extremes may change, especially in climatologically dry (low-humidity) regions. Here we show that specific humidity on dry days during summer (defined here as July–September) has decreased over the past seven decades in the United States Southwest, and that the greatest decreases co-occur with the hottest temperatures. Hot, dry summers have anomalously low evapotranspiration that is linked to low summer soil moisture. The recent decrease in summer soil moisture is explained by declines in June soil moisture, whereas the interannual variability is controlled by summer precipitation. Future projections of hot, dry days in the Southwest are uncertain due to the large spread in the Coupled Model Intercomparison Project phase 6 (CMIP6) trends in soil moisture and precipitation through 2100.

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Fig. 1: Changes in the distribution of specific humidity as a function of increasing global mean temperature anomalies (GMTA) and local temperature.
Fig. 2: Decreases in specific humidity in the American Southwest are amplified on hot days.
Fig. 3: The observed and fitted amplification index from 1950 to 2019.
Fig. 4: Increased probability of hot, dry days linked to reduced evapotranspiration and soil moisture.
Fig. 5: CMIP6 projections of June column soil moisture, summer precipitation and summer surface soil moisture.

Data availability

Data from ISD are publicly available at Data from ERA5 are publicly available at Data from JRA-55 are publicly available at (monthly) and (daily). Data from GPCC are publicly available at Data from GLDAS2.0 are publicly available at Model output from CMIP6 is publicly available at Berkeley Earth global mean temperature is publicly available at ERSSTv.5 data are publicly available at The AMV index was calculated using the Climate Variability and Diagnostics Package, which is publicly available at The PDO index is publicly available at The Niño 3.4 index is publicly available at

Code availability

Code to fit the non-crossing quantile smoothing splines model is available at Code to perform the analysis and reproduce the figures in the paper is available at


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K.A.M. acknowledges support from the National Science Foundation (award no. 1939988). I.R.S. was supported by the National Center for Atmospheric Research (NCAR), which is a major facility sponsored by the National Science Foundation under the Cooperative Agreement 1852977. JRA-55 data were provided by the Research Data Archive of the Computational and Information Systems Laboratory (CISL) at NCAR. Analysis was performed on the Casper cluster supported by by NCAR’s CISL. We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modelling groups (listed in Methods) for producing and making available their model output. For CMIP, the US Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals.

Author information




K.A.M. conceived the study, performed the analysis and wrote the manuscript. A.P. contributed to the development of the method and provided commentary on the manuscript. I.R.S. provided feedback on the analysis and manuscript. Correspondence should be addressed to K.A.M.

Corresponding author

Correspondence to Karen A. McKinnon.

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The authors declare no competing interests.

Additional information

Peer review informationNature Climate Change thanks Tim Cowan, Clemens Schwingshackl and the other, anonymous, reviewer(s) 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.

Extended data

Extended Data Fig. 1 Changes in hot, dry days for JRA-55, ERA5, ISD1979, and ERA5 at locations of Integrated Surface Database stations.

As in Fig. 2, but with data from (a, b) JRA-55, (c, d) ERA5, (e, f) Integrated Surface Database (ISD) stations from 1979–2019, and (g, h) ERA5 subset to the locations of the ISD stations in (e). Trends are calculated over the 1958–2019 period for JRA-55, and over the 1979–2019 period for all other maps. The ERA5-based trends in panel (d) uniquely do not show the amplification of drying at hot temperatures. To test whether the difference is due to the different starting point of the ERA5 trends, we recalculate trends in the ISD stations for the 1979–2019 period (panels e and f), which still show the amplification. To test whether the difference is due to the specific locations of the ISD stations, we subset the ERA5 trends to the locations of the ISD stations (panels g and h); this subset does not show the amplification behaviour. In the line plots (panels b, d, f, h), thin grey lines show the trends at each gridbox, and the thick black line is the area-weighted average across gridboxes. Note the different y-scales across the line plots.

Extended Data Fig. 2 Vertical and horizontal divergence do not explain the amplification index.

a, The composite vertical profile of July–August-September specific humidity from ERA5 on years in the top tercile (33%) of the amplification index minus the bottom tercile. b, The composite July–August-September vertically integrated moisture divergence from ERA5 on years in the top tercile of the amplification index minus the bottom tercile. c, The time series of the amplification index (orange) and the Southwest-average vertically integrated moisture divergence (teal). (d-i) As in (b)-(c), but for July, August, and September vertically integrated moisture divergence separately. Vertically integrated moisture divergence is the total per day.

Extended Data Fig. 3 Runoff covaries with surface soil moisture, but its contribution to the water balance is small.

As in Fig. 4, but for runoff from ERA5. Runoff is the total per day.

Extended Data Fig. 4 The CMIP6 models can have large biases in their mean state of precipitation, soil moisture, and evapotranspiration in the Southwest.

The distribution of July–August-September (a) average precipitation, (b) surface soil moisture, and (c) evapotranspiration from the 28 CMIP6 models used for Fig. 5 (blue histograms) and ERA5 (red vertical line). The CMIP6 estimates are based on 1979–2014, and the ERA5 estimates are based on 1979–2019; the end date in CMIP6 is the end of the historical scenario simulations. The 95% range shown for ERA5 is calculated by performing a bootstrap of the seasonal mean values with replacement, and provides an estimate of the uncertainty of the mean value due to sampling of internal variability. Surface soil moisture in the CMIP6 models is calculated over the top 10cm, whereas the top soil layer in ERA5 is 7cm; as such, the ERA5 surface soil moisture is multiplied by 10/7 for this comparison.

Extended Data Fig. 5 Changes in hot, dry extremes in Integrated Surface Database stations from 1950–2019.

As in Fig. 2, but with the limited number of Integrated Surface Database stations with data beginning in 1950. Trends are calculated over the 1950–2019 period.

Extended Data Fig. 6 The complex seasonal structure of specific humidity is captured by ten harmonics.

The empirical seasonal cycle of specific humidity (blue), and three different estimates of the seasonal cycle using ten seasonal harmonics (orange), a low-pass Butterworth filter with a frequency cutoff of 1/30 day−1, and a 15-day moving average at two Integrated Surface Database stations. Tucson International Airport (a) is influenced by the North American monsoon, and shows a rapid change in specific humidity at its onset in July, whereas the seasonal cycle in specific humidity at Stockton Metropolitan Airport (b) does not show such rapid changes.

Extended Data Fig. 7 The amplification index using more extreme cutoffs for hot and dry days.

The ISD1973 amplification index used in the main text (blue) compared to a version calculated with more extreme temperature (90th-100th percentile) and specific humidity (less than 5th percentile) cutoffs (orange).

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McKinnon, K.A., Poppick, A. & Simpson, I.R. Hot extremes have become drier in the United States Southwest. Nat. Clim. Chang. 11, 598–604 (2021).

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