Articles | Volume 28, issue 3
Nonlin. Processes Geophys., 28, 329–346, 2021
Nonlin. Processes Geophys., 28, 329–346, 2021

Research article 29 Jul 2021

Research article | 29 Jul 2021

Improving the potential accuracy and usability of EURO-CORDEX estimates of future rainfall climate using frequentist model averaging

Stephen Jewson et al.

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Subject: Predictability, probabilistic forecasts, data assimilation, inverse problems | Topic: Climate, atmosphere, ocean, hydrology, cryosphere, biosphere | Techniques: Theory
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Cited articles

Barnes, E. A., Hurrell, J. W., Ebert-Uphoff, I., Anderson, C., and Anderson, D.: Viewing Forced Climate Patterns Through an AI Lens, Geophys. Res. Lett., 46, 13389–13398, 2019. 
Benestad, R., Haensler, A., Hennemuth, B., Illy, T., Jacob, D., Keup-Thiel, E., Kotlarski, S., Nikulin, G., Otto, J., Rechid, D., Sieck, K., Sobolowski, S., Szabó, P., Szépszó, G., Teichmann, C., Vautard, R., Weber, T., and Zsebeházi, G.: Guidance for EURO-CORDEX, available at: (last access: 9 January 2021), 2017. 
Bernardo, J. and Smith, A.: Bayesian Theory, Wiley, New York, ISBN 0 471 49464 X, 1993. 
Brocker, J. and Smith, L.: Scoring Probabilistic Forecasts: The Importance of Being Proper, Weather Forecast., 22, 382–388, 2007. 
Burnham, K. and Anderson, D.: Model Selection and Multimodel Inference, Springer-Verlag, New York, ISBN 978-1-4419-2973-0, 2002. 
Short summary
Climate model simulations are uncertain. In some cases this makes it difficult to know how to use them. Significance testing is often used to deal with this issue but has various shortcomings. We describe two alternative ways to manage uncertainty in climate model simulations that avoid these shortcomings. We test them on simulations of future rainfall over Europe and show they produce more accurate projections than either using unadjusted climate model output or statistical testing.