Rapid urbanisation along the coasts of the world in recent decades has increased their vulnerability to storm surges, especially in response to mean sea level rise. The unique geographical and social conditions of Copenhagen, a major European coastal city, have prompted urban expansion along Køge Bay to the south of the city. However, this new urbanisation area is confronted with the common obstacle of developing a coastal defence strategy, i.e., the lack of long-term observational data required to determine a reliable storm surge protection level. This study aims to address this issue by developing a framework that integrates historical records of extreme storm surge events into coastal defence strategies, using Copenhagen as a case study. 'Statistical Modelling and Forecasting' is one of the steps in our proposed four-step framework solution. Using Bayesian statistical methods, we fitted the historical storm surge data to appropriate probability distributions. This enabled us to generate probabilistic forecasts of storm surge magnitudes, providing insight into the likelihood of future events and their potential impacts on the coastal area. Bayesian MCMC methods offer a powerful framework for incorporating uncertainty and expert knowledge into extreme value analysis. By utilising prior distributions and combining them with the likelihood function, these methods enable the estimation of posterior distributions of model parameters. This is particularly advantageous when dealing with limited data, as expert opinions and historical knowledge can be effectively integrated. We outline the application of the aforementioned extreme value analysis techniques and Bayesian MCMC methods within our four-step framework to integrate historical storm surge events into coastal defence strategies.