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Abstract

Characterizing extreme sea levels in coastal areas (i.e., estimates for high return periods) and its regional climate variations is a requirement to better understand coastal hazards. In many coastal regions, however, in-situ data or accurate modeled maxima values are not available. Satellite altimetry provides almost 30 years of global historical sea level data. Nonetheless, analyzing extreme sea levels from satellite data poses challenges, including the contamination of altimeter measurements near the coast and the need to manage along-track and inter-calibrated multi-mission products. To address these challenges, we developed a global statistical approach that involves identifying extreme subsamples of non-tidal-residual data on coastal areas and modeling the extreme behavior using a time-dependent extreme generalized value (GEV) model. The method and results obtained are validated with tide-gauge records.This study presents an assessment of the climate variability of sea level extreme events at time scales ranging from monthly to the long-term all over the world coast. The study quantifies the seasonal variability of extreme sea levels and identifies the periods with the highest probability of experiencing such events. The impact of various climate teleconnection patterns, such as the Arctic Oscillation and ENSO, on the magnitude of extreme events is also investigated. Moreover, the study addresses the modeled trend of sea level extremes, demonstrating a general clear and significant increase over time. Finally, the relative contribution of non-tidal residual sea level component and the astronomical tide in sea level extremes is assessed, identifying the coastal regions predominantly controlled by each component.