In this paper, a neural-network-based super-resolution technique is applied to the reconstruction of significant wave height and other sea state variables calculated over coarse meshes by a spectral wave model. The potential of the technique is demonstrated in a case study and the efficiency of the training process as well as the requirements with respect to data quality are analyzed. In this particular example, reasonable accuracy is achieved using only one year of training data with the help of traditional Machine Learning methods like Transfer Learning and Data Augmentation. The presented method leads to up to 50-times lower computation time in comparison to an equivalent traditional direct modeling approach at fine resolution. Overall, incorporation of the presented method into major wave forecasting systems has the potential to allow for the creation of “zoomed-in’ areas of interest without the requirement for supplementary calculations at higher resolution.