A low-lying country as The Netherland is prone to coastal flooding, and its risk may be enhanced by global-warming induced climate change. Sea level rise has been historically considered as the key factor in coastal retreat, but waves also play an important erosive role along the coast, which can also be affected by the changing climate. During the last years, important advances have been achieved in climate modeling, with a very detailed characterization of the different components of the climate system, for the present and for different future scenarios. However, characterization of future ocean waves is still a matter of discussion and ongoing research. In this thesis, a statistical downscaling methodology based on weather types has been chosen to model the present wave climate and explore potential changes in future waves. These changes are quantified in terms of the impact of these variations in the longshore sediment transport. The methodology is applied to Noordwijk, selected as a representative location of the central Dutch coast. The statistical downscaling methodology is based on a classification procedure of the predictor into similar atmospheric patterns over the wave generation areas, namely the weather types. Then, the wave data is grouped according to the occurrence of the weather types. The predictor is built from the sea level pressure fields (SLP) and the squared SLP gradients, while the predictant wave climate is characterized by significant wave height, wave peak period and mean wave direction of wind sea and swell components, resulting in unimodal or bimodal sea states. The chronology of the weather types is modeled using an autoregressive logistic model, which incorporates the seasonality, the interannual variability and the persistence observed from the historical data. For each weather type, wave parameters are modeled using the categorical distribution for the sea-state type, non-parametric kernel density functions for the central-mass regime and two generalized Pareto distributions for the lower and upper tails of wave height and wave period data. The statistical dependence between wave parameters for each sea state is included using a vine-copula approach, where the bivariate dependence of H_s and T_p is modeled using the AC skew t copula and the remaining relations are considered to be Gaussian. The effect of climate change is studied using SLP predictors from the global circulation model ACCESS1.0, under the RCP8.5 scenario and period 2070-2099. The statistical model is applied to identify changes in the occurrence probability of the weather types in the future. The importance of these changes are quantified in terms of the wave-induced longshore sediment transport using the process based model Unibest TC. The longshore sediment transport distribution for each weather type is computed and afterwards the changes in gross and net transports are estimated using the present and future probabilities of the weather types. In terms of the weather types, the results of this work suggest changes in the occurrence probability of the weather types in the future, with variations of ±60% with respect to present climate, and no relevant changes in the sequence of weather types, neither in terms of the transition probability matrix nor in terms of the persistence of each weather type. In terms of longshore sediment transport, significant changes are detected in the transport associated with some of the weather types. For the remaining weather types, the changes are in the order of the model uncertainty. Taking into account the contributions from all weather types, a net increase of the southward directed net longshore sediment transport with respect to the historical period is detected. This increase is driven by a decrease in northwards transport and a lower decrease in southwards transport. This result is in line with a poleward shift of the North Atlantic storm track reported in other studies. Most importantly, weather-type based climate classification has in this thesis been succesfully proven to be a reliable tool to analyze the wave climate at the location of interest. Furthermore, the statistical downscaling also provides a climate emulator that captures the climate dynamics at different time scales, which can be used for stochastic simulations of the atmospheric and wave climate, either for the recent past or future projections.