Non-stationary Extreme Value Analysis (NEVA) allows to determine the probability of exceedance of extreme sea states taking into account trends in the time series of data at hand. In this work, we analyse the reliability of NEVA of significant wave height ($H_s$) and peak period ($T_p$) under the assumption of linear trend for time series of annual maxima (AM) $H_s$ in the Mediterranean Sea. A methodology to assess the significance of the results of the non-stationary model employed is proposed. Both the univariate long-term extreme value distribution of $H_s$ and the bivariate distribution of $H_s$ and $T_p$ are considered. For the former, a non-stationary Generalized Extreme Value (GEV) probability is used, and a methodology to compute the parameters of the distribution based on the use of a penalty function is explored. Then, non-stationary GEV is taken as a reference to compute the Environmental Countours of $H_s$ and $T_p$, assuming a conditional model for the latter parameter. Several methods to compute linear trends are analysed and cross-validated on the series of AM $H_s$ at more than 20,000 hindcast nodes. Results show that the non-stationary analysis provides advantages over the stationary analysis only when all the considered metrics are consistent in indicating the presence of a trend. Moreover, both the univariate return levels of $H_s$ and bivariate return levels of $H_s$ and $T_p$ show a marked dependence to the time window considered in the GEV distribution formulation. Therefore, when applying NEVA for coastal and marine applications, the hypothesis of linear trend and the length of the reference data used for the non-stationary distribution should be carefully considered.

非平稳极值分析（NEVA）可以考虑到手头数据的时间序列趋势，确定超出极端海况的概率。在这项工作中，我们分析了显着波高的NEVA的可靠性（$H_s$）和高峰期（${Ť}_p$）在年最大值（AM）时间序列的线性趋势假设下 $H_s$在地中海。提出了一种方法来评估所采用的非平稳模型的结果的重要性。两者的单变量长期极值分布$H_s$ 和的二元分布 $H_s$ 和 ${Ť}_p$被考虑。对于前者，使用非平稳的广义极值（GEV）概率，并探索了一种基于惩罚函数的计算分布参数的方法。然后，将非平稳GEV作为计算环境污染指数的参考。$H_s$ 和 ${Ť}_p$，假设后一个参数为条件模型。在AM系列上分析和交叉验证了几种计算线性趋势的方法$H_s$在超过20,000个后播节点上。结果表明，仅当所有考虑的指标在指示趋势的存在方面保持一致时，非平稳分析才提供优于平稳分析的优势。而且，两个单变量收益水平$H_s$ 和二元回报水平 $H_s$ 和 ${Ť}_p$显示对GEV分布公式中考虑的时间窗口有明显的依赖性。因此，在将NEVA用于沿海和海洋应用时，应仔细考虑线性趋势的假设以及用于非平稳分布的参考数据的长度。