Recent climate extremes have broken long-standing records by large margins. Such extremes unprecedented in the observational period often have substantial impacts due to a tendency to adapt to the highest intensities, and no higher, experienced during a lifetime. Here, we show models project not only more intense extremes but also events that break previous records by much larger margins. These record-shattering extremes, nearly impossible in the absence of warming, are likely to occur in the coming decades. We demonstrate that their probability of occurrence depends on warming rate, rather than global warming level, and is thus pathway-dependent. In high-emission scenarios, week-long heat extremes that break records by three or more standard deviations are two to seven times more probable in 2021–2050 and three to 21 times more probable in 2051–2080, compared to the last three decades. In 2051–2080, such events are estimated to occur about every 6–37 years somewhere in the northern midlatitudes.
This is a preview of subscription content
Subscribe to Nature+
Get immediate online access to the entire Nature family of 50+ journals
Subscribe to Journal
Get full journal access for 1 year
only $8.25 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Get time limited or full article access on ReadCube.
All prices are NET prices.
All original CMIP5 and CMIP6 data, the CanESM2 and NCAR LENS ensembles, and the ERA5 reanalysis used in this study, are publicly available as follows: CMIP5 model data, https://esgf-node.llnl.gov/projects/cmip5/; CMIP6 model data, https://esgf-node.llnl.gov/projects/cmip6/; CanESM2 ensemble reanalysis, https://open.canada.ca/data/en/dataset/aa7b6823-fd1e-49ff-a6fb-68076a4a477c; NCAR LENS ensemble, https://www.cesm.ucar.edu/projects/community-projects/LENS/data-sets.html; and ECMWF5 ERA5 reanalysis, https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels. The output from CESM1.2 and CESM-CAM4 used in this analysis is available at https://data.iac.ethz.ch/Fischer_et_al_2021_RecordExtremes/.
All computer code to reproduce the main results and all figures are available at https://data.iac.ethz.ch/Fischer_et_al_2021_RecordExtremes/
Risser, M. D. & Wehner, M. F. Attributable human-induced changes in the likelihood and magnitude of the observed extreme precipitation during Hurricane Harvey. Geophys. Res. Lett. 44, 12457–12464 (2017).
Emanuel, K. Assessing the present and future probability of Hurricane Harvey’s rainfall. Proc. Natl Acad. Sci. USA 114, 12681–12684 (2017).
Van Oldenborgh, G. J. et al. Attribution of extreme rainfall from Hurricane Harvey, August 2017. Environ. Res. Lett. 12, 124009 (2017).
Overland, J.E. & Wang, M. The 2020 Siberian heat wave. Int. J. Climatol. 41, E2341–E2346 (2020).
Miralles, D. G., Teuling, A. J., Van Heerwaarden, C. C. & Vilà-Guerau de Arellano, J. G. Mega-heatwave temperatures due to combined soil desiccation and atmospheric heat accumulation. Nat. Geosci. 7, 345–349 (2014).
Barriopedro, D., Fischer, E. M., Luterbacher, J., Trigo, R. M. & García-Herrera, R. The hot summer of 2010: redrawing the temperature record map of Europe. Science 332, 220–224 (2011).
Robine, J. M. et al. Death toll exceeded 70,000 in Europe during the summer of 2003. C. R. Biol. 331, 171–178 (2008).
Garcia-Herrera, R., Trigo, R. M., Luterbacher, J., Schär, C. & Fischer, E. M. A review of the European summer heat wave of 2003. Crit. Rev. Environ. Sci. Technol. 40, 267–306 (2010).
Haden, V. R., Niles, M. T., Lubell, M., Perlman, J. & Jackson, L. E. What attitudes and beliefs motivate farmers to mitigate and adapt to climate change? PLoS ONE 7, e52882 (2012).
Blennow, K., Persson, J., Tomé, M. & Hanewinkel, M. Climate change: believing and seeing implies adapting. PLoS ONE 7, e50182 (2012).
Weber, E. U. Experience-based and description-based perceptions of long-term risk: why global warming does not scare us (yet). Climatic Change 77, 103–120 (2006).
Coumou, D., Robinson, A. & Rahmstorf, S. Global increase in record-breaking monthly-mean temperatures. Climatic Change 118, 771–782 (2013).
Meehl, G. A., Tebaldi, C., Walton, G., Easterling, D. & McDaniel, L. Relative increase of record high maximum temperatures compared to record low minimum temperatures in the U.S. Geophys. Res. Lett. 36, L23701 (2009).
Elguindi, N., Rauscher, S. A. & Giorgi, F. Historical and future changes in maximum and minimum temperature records over Europe. Climatic Change 117, 415–431 (2013).
King, A. D. Attributing changing rates of temperature record breaking to anthropogenic influences. Earth’s Future 5, 1156–1168 (2017).
Power, S. B. & Delage, F. P. D. Setting and smashing extreme temperature records over the coming century. Nat. Clim. Change 9, 529–534 (2019).
Perkins, S. E., Alexander, L. V. & Nairn, J. R. Increasing frequency, intensity and duration of observed global heatwaves and warm spells. Geophys. Res. Lett. 39, L20714 (2012).
Kunkel, K. E., Liang, X.-Z., Zhu, J. & Lin, Y. Can CGCMs simulate the twentieth-century “warming hole” in the central United States? J. Clim. 19, 4137–4153 (2006).
Meehl, G. A., Arblaster, J. M. & Branstator, G. Mechanisms contributing to the warming hole and the consequent US east–west differential of heat extremes. J. Clim. 25, 6394–6408 (2012).
Yu, S. et al. Attribution of the United States ‘warming hole’: aerosol indirect effect and precipitable water vapor. Sci. Rep. 4, 6929 (2014).
Davison, A. C. & Huser, R. Statistics of extremes. Annu. Rev. Stat. Its Application 2, 203–235 (2015).
Sun, Y. et al. Rapid increase in the risk of extreme summer heat in Eastern China. Nat. Clim. Change 4, 1082–1085 (2014).
Mueller, B., Zhang, X. & Zwiers, F. W. Historically hottest summers projected to be the norm for more than half of the world’s population within 20 years. Environ. Res. Lett. 11, 044011 (2016).
Christidis, N., Jones, G. S. & Stott, P. A. Dramatically increasing chance of extremely hot summers since the 2003 European heatwave. Nat. Clim. Change 5, 46–50 (2015).
Moore, F. C., Obradovich, N., Lehner, F. & Baylis, P. Rapidly declining remarkability of temperature anomalies may obscure public perception of climate change. Proc. Natl Acad. Sci. USA 116, 4905–4910 (2019).
Changnon, S. A., Kunkel, K. E. & Reinke, B. C. Impacts and responses to the 1995 heat wave: a call to action. Bull. Am. Meteorol. Soc. 77, 1497–1506 (1996).
Meehl, G. A. & Tebaldi, C. More intense, more frequent, and longer lasting heat waves in the 21st century. Science 305, 994–997 (2004).
Seneviratne, S. I., Pal, J. S., Eltahir, E. A. B. & Schär, C. Summer dryness in a warmer climate: a process study with a regional climate model. Clim. Dynam. 20, 69–85 (2002).
Horton, R. M., Mankin, J. S., Lesk, C., Coffel, E. & Raymond, C. A review of recent advances in research on extreme heat events. Curr. Clim. Change Rep. 2, 242–259 (2016).
Schumacher, D. L. et al. Amplification of mega-heatwaves through heat torrents fuelled by upwind drought. Nat. Geosci. 12, 712–717 (2019).
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).
Seneviratne, S. I. et al. Investigating soil moisture–climate interactions in a changing climate: a review. Earth Sci. Rev. 99, 125–161 (2010).
Rahmstorf, S. & Coumou, D. Increase of extreme events in a warming world. Proc. Natl Acad. Sci. USA 108, 17905–17909 (2011).
Seneviratne, S. I., Donat, M. G., Pitman, A. J., Knutti, R. & Wilby, R. L. Allowable CO2 emissions based on regional and impact-related climate targets. Nature 529, 477–483 (2016).
Fischer, E. M., Sedláček, J., Hawkins, E. & Knutti, R. Models agree on forced response pattern of precipitation and temperature extremes. Geophys. Res. Lett. 41, 8554–8562 (2014).
Pendergrass, A. G., Lehner, F., Sanderson, B. M. & Xu, Y. Does extreme precipitation intensity depend on the emissions scenario? Geophys. Res. Lett. 42, 8767–8774 (2015).
Taleb, N. N. The black swan: the impact of the highly improbable. Rev. Austrian Econ. 21, 361–364 (2007).
Lin, N. & Emanuel, K. Grey swan tropical cyclones. Nat. Clim. Change 6, 106–111 (2016).
Fouillet, A., Rey, G. & Laurent, F. Excess mortality related to the August 2003 heat wave in France. Int. Arch. Occup. Environ. Health 80, 16–24 (2006).
Green, H. K., Andrews, N., Armstrong, B., Bickler, G. & Pebody, R. Mortality during the 2013 heatwave in England—how did it compare to previous heatwaves? A retrospective observational study. Environ. Res. 147, 343–349 (2016).
Wetter, O. et al. The largest floods in the High Rhine basin since 1268 assessed from documentary and instrumental evidence. Hydrol. Sci. J. 56, 733–758 (2011).
Sippel, S. et al. Quantifying changes in climate variability and extremes: pitfalls and their overcoming. Geophys. Res. Lett. 42, 9990–9998 (2015).
Fischer, E. M., Beyerle, U. & Knutti, R. Robust spatially aggregated projections of climate extremes. Nat. Clim. Change 3, 1033–1038 (2013).
Deser, C., Phillips, A. S., Alexander, M. A. & Smoliak, B. V. Projecting North American climate over the next 50 years: uncertainty due to internal variability. J. Clim. 27, 2271–2296 (2014).
Kirchmeier-Young, M. C., Zwiers, F. W. & Gillett, N. P. Attribution of extreme events in Arctic Sea ice extent. J. Clim. 30, 553–571 (2017).
Kay, J. E. et al. The Community Earth System Model (CESM) large ensemble project: a community resource for studying climate change in the presence of internal climate variability. Bull. Am. Meteorol. Soc. 96, 1333–1349 (2015).
Swart, N. et al. The Canadian Earth System Model version 5 (CanESM5.0.3). Geosci. Model Dev. Discuss. 12, 4823–4873 (2019).
Ana, F. & De Haan, L. On the block maxima method in extreme value theory: PWM estimators. Ann. Stat. 43, 276–298 (2015).
Falk, M., Chokami, A. K. & Padoan, S. A. Some results on joint record events. Stat. Probab. Lett. 135, 11–19 (2018).
Falk, M., Khorrami Chokami, A. & Padoan, S. Records for time-dependent stationary Gaussian sequences. J. Appl. Probab. 57, 78–96 (2020).
Ahsanullah M. & Nevzorov V.B. in International Encyclopedia of Statistical Science (Ed. Lovric, M.) 1195–1202 (Springer, 2011).
We acknowledge funding received from the Swiss National Science Foundation (grant no. 200020_178778) (E.M.F). We acknowledge the World Climate Research Programme, which, through its Working Group on Coupled Modelling, coordinated and promoted CMIP5 and CMIP6. We thank the climate modelling groups for producing and making available their model output. We acknowledge Environment and Climate Change Canada’s Canadian Centre for Climate Modelling and Analysis for executing and making available the CanESM2 large ensemble simulations. We acknowledge the US CLIVAR working group on large ensembles for compiling the Multi-Model Large Ensemble Archive.
The authors declare no competing interests.
Peer review information Nature Climate Change thanks Raphaël Huser 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.
Regional average Tx7day time series for (top) the European 2003 heatwave region 35–60°N and 10°W-20°E, (middle) the CNA region used in Fig. 1, (bottom) the Russian 2010 heatwave region 40–70°N and 20–70°E. The solid line shows a regional average of EOBS v19 gridded observations38 and the dashed lines of ERA5 reanalysis product39. The two dots illustrate two record-shattering events, and the magnitude is expressed in standard deviations of the detrended time series.
Same as Fig. 1b but based on CanESM2 large ensemble. Yellow, orange and dark red lines illustrate the non-stationary GEV estimates for 200-, 500-, and 1000-yr return periods from all members using global mean temperature as a covariate for the location and scale parameter (see Methods). Best estimate of 1000-yr return period estimated from a stationary GEV fit from all 50 members (dotted violet line) (1950–2019) along with 95% CIs.
(a-c) Tx7day anomaly and (d-f) associated 500 hPa geopotential during the 2003 European summer heatwave, the 1995 Chicago heatwave and the 2010 Russian heatwave. Anomalies are calculated based on the ECMWF ERA5 reanalysis and expressed as anomalies relative to the period 1986–2005.
(top) Tx7day anomalies averaged across Central N America in all CESM1.2 members from 1850–2100. The red and yellow dots illustrate the two record-shattering events discussed in the main text. The red line and red dot highlight the event and corresponding simulations shown in Fig. 1a. The yellow line and yellow dot highlight the second most extreme record-shattering event. (bottom) The second most intense record-shattering event discussed in the main text is illustrated in the same way as the most extreme event in Fig. 1a. Best estimate of 1,000-yr return period estimated from a stationary GEV fit to the selected member (1850–2019) (dashed turquoise line) and from all 84 members (dotted violet line) (1950–2019) along with 95% CI.
Same as Fig. 2d–g but relative to a constant reference period expressed as the multimember average across 1981–2010. The record-shattering extremes exceeding the previous record by more than 2σ, 3σ, and 4σ are marked with yellow, orange and red dots, respectively.
Extended Data Fig. 6 Examples of record-shattering events in other large ensembles and other regions.
Record-shattering event (red dot) and annual maximum 7-day temperature anomalies averaged over (a,c,e) Central N America) and (b,d,f) Central Europe in the corresponding simulation before the event (black line).
About this article
Cite this article
Fischer, E.M., Sippel, S. & Knutti, R. Increasing probability of record-shattering climate extremes. Nat. Clim. Chang. 11, 689–695 (2021). https://doi.org/10.1038/s41558-021-01092-9
Unprecedented Heatwave in Western North America during Late June of 2021: Roles of Atmospheric Circulation and Global Warming
Advances in Atmospheric Sciences (2022)
Environmental Science and Pollution Research (2022)
Environmental effects of stratospheric ozone depletion, UV radiation, and interactions with climate change: UNEP Environmental Effects Assessment Panel, Update 2021
Photochemical & Photobiological Sciences (2022)
Stochastic Environmental Research and Risk Assessment (2021)