The position of the intertropical convergence zone (ITCZ) may lead to drying/flooding in some parts of the world. Its spatial and temporal variation responds to well-established oscillation processes like the El-Niño-Southern Oscillation (ENSO). This research establishes the response of the ITCZ position to the ENSO phases and how its position, determined from maximum precipitation, relates to the convergence of surface tropical winds. The ERA5 reanalysis data, 1990 – 2020, is used in this study. Each longitude is scanned for latitude of maximum precipitation, during each El-Niño/La-Niña/Neutral years, within the 20°N/S latitude range to identify the ITCZ position. The study concludes that the position established by the maximum precipitation aligns with the surface tropical winds convergence over the global oceanic areas. On seasonal average, the La-Niña related ITCZ position is consistently southward of its El-Niño position over Africa and Central Pacific Ocean. The study uncovered that the extreme cases of El-Niño/La-Niña leads to further north/south shifting of the ITCZ position from its normal El-Niño/La-Niña positions. The continental and Atlantic Ocean ITCZ is more persistent and shows a minimal fluctuation during the El-Niño/La-Niña. Over Africa, cross-wavelet analysis shows common high-power features in the Oceanic Niño Index (ONI) and ITCZ signals over a four-year periodicity, mirroring the ENSO periodicity albeit with slowly varying time lag across the years. The cross-correlation of the two signals is strongest in Austral summer (DJF). The global and temporal ITCZ shifts open an opportunity for improved interpretation of seasonal forecasts of hydroclimatic events, especially under climate change conditions

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.