Extreme value analyses of sea-level using tide-gauge measurements usually suffer from limited effective duration of observation which can result in large uncertainties, especially when outliers are present. To tackle this issue, a Bayesian MCMC method is developed integrating historical data in extreme sea-level analyses. A real case study shows a significant improvement in return values estimation and the usefulness of the Bayesian framework to predict future annual exceedance probabilities.