Due to the unprecedented population and economic development along the coastal zone all over the world, knowledge about future extreme oceanographic events will assist in ensuring human and property safety, a task with increasing significance in the light of projected climate change impacts. A joint estimation of extreme storm events variates of deep water wave conditions was performed. It can be used for multivariate description of wave climate variates, such as wave height, period, steepness, and storm duration, etc. These storm process elements can be simulated and extrapolated from limited observational data for optimal structure protection strategies and various disaster risk analysis, like erosion or overtopping. The analysis shows state of the art probabilistic model improves probabilistic design reliability in coastal risk assessment framework and comprehensive management planning. Using the Monte-Carlo method and three construction methods of dependency structure, based on copula function, physical relationship and extreme value theory, the marginal probabilistic distribution functions of wave climate variables and the joint probability were derived. These approaches were employed using the Dutch offshore observation data from 1979 to 2009 of buoys near the Noordwijk beach. The simulated data group performs a reasonable similarity to the field measurements.