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\begin{document}

\title{Impact-based Skill Evaluation of Seasonal Precipitation Forecasts}



\author[1]{Zahir Nikraftar}%
\author[1]{Rendani Mbuvha}%
\author[2]{Mojtaba Sadegh}%
\author[3]{Willem Adolf Landman}%
\affil[1]{Queen Mary University of London}%
\affil[2]{Boise State University}%
\affil[3]{Council for Scientific and Industrial Research}%


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  \date{May 23, 2024}


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\begin{abstract}
Forecasting hydroclimatic extremes holds significant importance
considering the increasing trends in natural cascading climate-induced
hazards such as wildfires, floods, and droughts. This study evaluates
the performance of five Copernicus Climate Change Service (C3S) seasonal
forecast models (i.e., CMCC, DWD, ECCC, UK-Met, and M\selectlanguage{ngerman}étéo-France) in
predicting extreme precipitation events from 1993 to 2016 using 28
extreme precipitation indices reflecting timing and intensity of
precipitation in a seasonal timescale. We design indices using various
precipitation thresholds to reflect model skill in capturing the
distribution and intensity of precipitation over a season. We use
percentage bias, the Kendall Tau rank correlation, and ROC scores for
skill evaluation. We introduce an impact-based framework to evaluate
model skill in capturing extreme events over regions prone to natural
disasters such as floods and wildfires. The performance of models varies
across regions and seasons. The model skill is highlighted primarily in
the tropical and inter-tropical regions, while skill in extra-tropical
regions is markedly lower. Elevated precipitation thresholds correlate
with heightened model bias, revealing deficiencies in modelling severe
precipitation events. The impact-based framework analysis highlights the
superior predictive capabilities of the UK-Met and Météo-France models
for extreme event forecasting across many regions and seasons. In
contrast, other models exhibit strong performance in specific regions
and seasons. These results advance our understanding of an impact-based
framework in capturing a broad spectrum of extreme climatic events
through the strategic amalgamation of diverse models across different
regions and seasons, offering valuable insights for disaster management
and risk analysis.%
\end{abstract}\selectlanguage{ngerman}%



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\textbf{Hosted file}

\verb`979728_0_art_file_11667169_s54qy2.docx`  available at \url{https://authorea.com/users/708832/articles/693177-impact-based-skill-evaluation-of-seasonal-precipitation-forecasts}

\textbf{Hosted file}

\verb`979728_0_supp_11597974_s4zxgk.docx`  available at \url{https://authorea.com/users/708832/articles/693177-impact-based-skill-evaluation-of-seasonal-precipitation-forecasts}

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