Tide gauge records are the primary source for coastal sea level research, yet they are short, gappy, and affected by instrumental errors, which limits the study of extreme events and their long-term changes. We present MAROR (<strong>M</strong>ultipoint <strong>A</strong>utoregressive <strong>R</strong>econstruction <strong>O</strong>f sea-level <strong>R</strong>ecords), a neural network trained on tide gauge records that reconstructs gap-free, hourly coastal sea level from 1940 to the present, driven only by atmospheric forcing and, when available, satellite altimetry. Building on the HIDRA3, a convolutional neural network designed for short-term predictions, MAROR turns a short-range forecast model into a long-range autoregressive reconstruction engine, with an exposure-bias correction that removes drift and a generative ensemble that yields a calibrated, per-tide-gauge uncertainty band. We evaluate it on 373 tide gauges across eleven regions spanning a wide range of surge regimes, from extratropical shelves to tropical cyclones and western boundary currents. MAROR reconstructs the hourly storm surge with a median correlation of 0.88, reproduces the most extreme events, and outperforms both a hydrodynamic hindcast and a state-of-the-art deep-learning model.