On the ability of statistical wind - wave models to capture the variability and 1 long - term trends of the North Atlantic winter wave climate 2

11 A dynamical wind-wave climate simulation covering the North Atlantic Ocean and 12 spanning the whole 21 century under the A1B scenario has been compared with a set 13 of statistical projections using atmospheric variables or large scale climate indices as 14 predictors. As a first step, the performance of all statistical models has been evaluated 15 for the present-day climate; namely they have been compared with a dynamical wind16 wave hindcast in terms of winter Significant Wave Height (SWH) trends and variance 17 as well as with altimetry data. For the projections, it has been found that statistical 18 models that use wind speed as independent variable predictor are able to capture a larger 19 fraction of the winter SWH inter-annual variability (68% on average) and of the long 20 term changes projected by the dynamical simulation. Conversely, regression models 21 using climate indices, sea level pressure and/or pressure gradient as predictors, account 22 for a smaller SWH variance (from 2.8% to 33%) and do not reproduce the dynamically 23 projected long term trends over the North Atlantic. Investigating the wind-sea and swell 24 components separately, we have found that the combination of two regression models, 25 one for wind-sea waves and another one for the swell component, can improve 26 significantly the wave field projections obtained from single regression models over the 27