Articles | Volume 6, issue 2
18 Nov 2020
 | 18 Nov 2020

Nonstationary extreme value analysis for event attribution combining climate models and observations

Yoann Robin and Aurélien Ribes

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Cited articles

Annan, J. D. and Hargreaves, J. C.: Reliability of the CMIP3 ensemble, Geophys. Res. Lett., 37, 2,, 2010. a
CMIP5: CLIVAR Exchanges – Special Issue: WCRP Coupled Model Intercomparison Project – Phase 5 – CMIP5, Project Report 56, available at: (last access: 9 November 2020), 2011. a, b, c
Coles, S., Bawa, J., Trenner, L., and Dorazio, P.: An introduction to statistical modeling of extreme values, vol. 208, Springer Series in Statistics, Springer-Verlag, London, 2001. a
Cornes, R. C., van der Schrier, G., van den Besselaar, E. J. M., and Jones, P. D.: An Ensemble Version of the E-OBS Temperature and Precipitation Data Sets, J. Geophys. Res.-Atmos., 123, 9391–9409,, 2018. a
Eaton, M. L.: Multivariate statistics: a vector space approach, John Wiley & Sons, INC., 605 Third Ave., New York, NY 10158, USA, 1983, 512, 1983. a
Short summary
We have developed a new statistical method to describe how a severe weather event, such as a heat wave, may have been influenced by climate change. Our method incorporates both observations and data from various climate models to reflect climate model uncertainty. Our results show that both the probability and the intensity of the French July 2019 heatwave have increased significantly in response to human influence. We find that this heat wave might not have been possible without climate change.