Abstract Accurate estimation of extreme wind and wave conditions is critical for ocean engineering activities and applications. Various renewable energy offshore structures, particularly floating wind turbines are designed to sustain extreme wind and wave induced loads. Statistics of wind speeds and wave heights is the key input for structural safety and reliability study. Consequently, development of novel robust methods, able to predict extreme wind-wave conditions is essential. This paper discusses criteria for selecting design point by applying recently developed method for estimating extreme wave statistics, based on the hourly wave height and wind speed maxima at the location of interest. Wave and wind data, analyzed in this paper, was obtained from the hindcast model applied to the SEM-REV offshore sea location, near the coast of France, during years 2001–2010. The ECMWF (European Centre for Medium range Weather Forecasting) framework along with the atmospheric model SKIRON were employed to generate accurate hindcast wind-wave hourly data at the location of interest. Note that the SEM-REV site was built within the framework of the CPER (Contrat de Projet Etat-Region) 2007–2013 for the Pays de la Loire region, therefore it is important to note that 2001–2010 data studied in this paper was obtained by hindcast into the time period before SEM-REV began operations. Structural design values are often based on univariate statistical analysis, while actually multivariate statistics is more appropriate for modelling the whole structure. The bivariate analysis of extremes is often poorly understood and generally not adequately considered in most practical measurements/situations, therefore it is important to utilize recently developed bivariate average conditional exceedance rate (ACER2D) method. This paper studies extreme wind speeds and wave heights, that are simultaneously obtained at the same location. Due to less than full correlation between wind speed and wave height, application of the multivariate, or bivariate in the simplest case, extreme value theory is of practical importance. This paper focuses on application of the bivariate ACER2D method for prediction of bivariate extreme value statistics. Finally, this paper suggests how the design point should be chosed based on bivariate analysis. The latter is of particular engineering importance as it presents first application of bivariate wind-wave statistics to a raw SEM-REV site data.