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 Météo-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.
