Forecast downscaling for accurate nearshore wave conditions is important for a range of marine and coastal users. However, it is complex and high-resolution forecasting methods are computationally intensive, especially when being applied at scale. Identifying methods of downscaling to a high-resolution with accuracy and increased computational efficiency is desired. The prediction of wave parameters using machine learning techniques is becoming increasingly popular, potentially offering more computational efficiency. Other computationally efficient techniques also exist such as the Hybrid Downscale Technique and lookup table approaches. This paper compares three data-driven methods for downscaling offshore wave conditions to a typical computationally intensive process-based approach. A typical calibrated process-based wave model was used to create a catalogue of data from which to develop data-driven models. The comparison was also extended to include the use of wave parameter observations. Each of the data-driven methods showed comparable or improved numerical performance to the process driven technique particularly for the Hs parameter (with an up to 16% improvement in Root Mean Squared Error over the process based model). Both data-driven approaches were considerably more computationally efficient, between eleven and seven thousand times faster than the process-based model. However, limitations were identified that should be considered when applying data-driven approaches for predicting wave parameters, such as availability and consistency of data, and the ability of simple parameters to represent complex sea states.