Evaluating probabilistic forecasts of extremes using continuous ranked probability score distributions

International audienceVerification of probabilistic forecasts for extreme events has been a very active field of research, stirred by media and public opinions who naturally focus their attention on extreme events, and easily draw biased conclusions. In this context, classical verification methodologies tailored for extreme events, such as thresholded and weighted scoring rules, have undesirable properties that cannot be mitigated; the well-known Continuous Ranked Probability Score (CRPS) makes no exception.In this paper, we define a formal framework to assess the behavior of forecast evaluation procedures with respect to extreme events, that we use to point out that assessment based on the expectation of a proper score is not suitable for extremes. As an alternative, we propose to study the properties of the CRPS as a random variable using extreme value theory to address extreme events verification. To compare calibrated forecasts, an index is introduced that summarizes the ability of probabilistic forecasts to predictextremes. Its strengths and limitations are discussed using both theoretical arguments and simulations