## 1 Introduction  
Maritime structures, such as offshore facilities, breakwaters and other systems, are subjected to the effect of metocean variables, including extreme waves and extreme winds. These metocean variables can be considered as hazards that impose demands to these systems. Metocean variables can be characterized with a probability density function (PDF) frequently adopted from extreme value theory [1]. Among the metocean variables, extreme waves and winds can impact offshore facilities [2]; correlation of these two variables is important for design because they can occur simultaneously [3]. Recent studies on extreme waves and winds include the assessment of significant wave heights in the South China Sea using hindcast data [4], wind models to simulate waves in the South China Sea and the East China Sea [5], the use of radar images to estimate significant wave height [6], wave height forecasting during extreme events [7], assessment of wave heights by using generalized Pareto and Gumbel distributions [8], uncertainty assessment in extreme significant wave height by using different techniques [9] and a review on available wind-wave models and their limitations [10]. Additionally, hindcast for wind-wave for several decades was calculated and compared with observed data for estimating wave climate change and for risk analysis [11; 12]. Regarding studies on generalized extreme value (GEV) distribution, they are used lately to model different variables. For instance, GEV distributions resulted in a very adequate model of precipitation and evapotranspiration in India [13], and are also implemented in software to model annual maximum precipitation [14]; some variants of the method that consider nonstationarity are also found in the literature applied to river water levels [15]. The use of GEV for modeling extreme typhoon heading directions [16], joint PDFs for wave height and period [17] and for modeling extreme wave heights in the Mediterranean and at Portuguese locations [18; 19] are also reported in recent literature. This shows that GEV models are a tool used currently to effectively characterize metocean and other variables. Nevertheless, no single study in recent literature on GEV reports a proposal like the one contained in the present study. Studies regarding correlation of metocean variables are also available in the literature. Contour maps and joint distribution of environmental variables are used for marine and coastal design. Current approaches are summarized in [20], where nonparametric methods, copula models, hierarchical conditional models and conditional extremes models are used to compute contour maps for joint occurrence of significant wave height and wave period. Some of the important highlights reported in [20] include: that joint probabilities are important for reliability analysis as stipulated in guidelines and standards, that simultaneous occurrence of extreme events at prescribed return periods is of interest, that the values associated with such return periods are not uniquely defined (except by independent or perfectly correlated cases), that the problem may need to be studied in a case-by-case basis, that different techniques may be more suitable than others depending on the problem to be addressed, among other issues. Studies on correlated metocean variables accounting for seasonality are also found in the literature. For instance, [PERSON] [21] develops weekly contour maps for wave height and wave period using selected return periods. The season-specific variability of simultaneous metocean variables can be important for planning [21]. In [21], a proposal is based on normalizing the data using the mean and standard deviation to remove seasonality, which can be added back later; it is argued that the whole set of data (i.e., from all the seasons) can be taken advantage of, opposite to fitting the data into separate bins, which results in a decrease in the fitted data.