With the intensification of global climate warming, the probabilities and load intensities of extreme weather phenomenon are gradually increasing, which could threaten the safety of coastal and offshore infrastructures. The present study presents a joint probability distribution model of wind speed, wave height, wave period, wind direction and wave direction with Vine Copula function based on monitoring data from Lianyungang Ocean Station in the East China Sea. Firstly, the marginal probability distributions of wind and wave data are determined, in which the AIC criteria and RMSE index are employed to select the optimal probability distribution model and the maximum likelihood method is used to determine the model parameters. Subsequently, the optimal two-dimensional Copula function for wind and wave data is determined via the AIC criteria, and the model parameters are fitted with a Bayesian framework with a residual-based Gaussian likelihood function. To illustrate the goodness of fit, the binary frequency histogram of the original wind and wave data is compared with the proposed two-dimensional Copula function. Finally, the multi-dimensional joint probability distribution model of wind and wave data is established with the Vine Copula function based on the AIC criteria. The results show that the proposed Vine Copula model is able to describe the joint probability distribution between the wind speed, wave height, wave period, wind direction and wave direction.