A global dataset of bias-corrected waves and storm surge (1950–2023) with improved extremes for hazard mapping

Abstract. Consistent global datasets of storm surges and waves are essential for assessing coastal vulnerability under changing coastal hazard conditions. Building on the global coupled hindcast, we present an enhanced hazard-oriented dataset of storm surges and bulk and spectral peak wave parameters spanning 1950–2023. Modelled significant wave heights from the hindcast are bias-corrected using satellite altimetry observations, while storm-surge signals are derived from modelled sea surface heights through frequency-based filtering. The dataset includes significant wave height, wave period, wavelength, mean wave direction, directional spreading, and spectral characteristics of the three dominant energy peaks. We validate the corrected significant wave heights against observations from 485 buoy locations and the derived storm surges against 550 tide gauges, with particular emphasis on the upper tail of the distributions. For significant wave heights, the global median normalised bias beyond the 95th percentile is −0.88 %, indicating that systematic errors in extreme wave conditions are largely removed. At higher latitudes (30° N–65° N), where tide-gauge coverage is denser, storm surges show a median normalised bias beyond the 95th percentile of 2.62 % and a median correlation of 0.81, indicating that the modelling framework captures observed storm-surge variability effectively in well-observed extratropical regions, while larger uncertainties remain in tropical and equatorial regions. Additional event-based evaluation during tropical cyclones shows limited systematic bias in modelled peak wave heights and surge levels. The resulting dataset provides a consistent global baseline for coastal hazard mapping, non-stationary extreme value analysis, regional downscaling, compound-event assessment, and data-driven modelling applications. The dataset (Wadalkar et al., 2026) is available at https://doi.org/10.2905/JRC.W0YPDCR.