Assessing coastal hazards and planning robust defence infrastructure under a changing climate requires long-term, high-frequency sea-level records. However, historical tide gauge records are often temporally fragmented, while raw hydrodynamic hindcasts tend to systematically underestimate sub-hourly storm surge peaks due to spatial model resolution limitations. This paper introduces ReSEML v1.0 (Residual Surge Ensemble Machine Learning), a machine learning framework designed to reconstruct continuous, sub-hourly extreme sea-level catalogues across complex coastlines. By training a stacked deep learning ensemble (LSTM + TCN) to exclusively predict the local physical residual between sub-hourly tide gauge observations and a hydrodynamic model baseline, we build the continuous 64-year DEN-SURGE catalogue (1961–2024) across 50 Danish stations. By eliminating sensor malfunctions, closing multi-year monitoring gaps, and correcting numerical peak underestimations, DEN-SURGE delivers an AI-ready benchmark whose quality and physical completeness surpass both raw simulations and fragmented gauge records. Due to regional characteristics, corrections are applied by partitioning the coastline into 10 clusters. Out-of-sample validation shows that ReSEML reduces nationwide mean bias during high water events to within ± 0.4 cm and lowers Root Mean Squared Errors (RMSE) for the same events below 10 cm. Cross-referencing against independent historical storm surge lists confirms the framework's reliability in capturing historical peaks and recovering missing extremes. Extending the continuous record back to 1961 stabilises Extreme Value Analysis (EVA) curves, reducing 95 % confidence intervals for the 100-year return level by on average 46 %. While focused here on historical catalogue homogenisation, this work also provides a methodological bridge for downscaling future climate simulations, establishing a consistent framework to quantify evolving climate risks in coastal planning.