This work presents a new methodology for the medium to long-term stochastic forecasting of the main variables and indexes related to the wave climate that are involved in the decision-making process to allocate, operate and maintain individual nearshore wave energy converters (WECs) and/or wave farms. Compared to the state-of-the-art approaches, this methodology includes the assessment of the uncertainty by means of Monte Carlo simulations, constituting a valuable step forward. The methodology is based on the simulation of N-y-year time series of wave climate variables that maintain the same statistical descriptors and seasonal and year-to-years variations of a hindcasted time series. This step is repeated N-e times to provide a sample size large enough to assess the uncertainty of the predictions. Because the wave energy resource is obtained from the nearshore, a large amount of wave propagations would be required. However, our methodology incorporates downscaling techniques that significantly improve the computational efficiency, and only a reduced number of N-w sea states should be propagated using an advanced numerical model. The methodology was applied to Playa Granada beach (southern Spain), obtaining the wave energy resource at 24 locations in the nearshore for 25-year time series repeated 1000 times. The selection of the most promising location for WECs on the basis of hindcasted or forecasted data provides different results. This highlights the importance of the proposed methodology for the advanced planning and design of any prospective energy extraction project. (C) 2016 Elsevier Ltd. All rights reserved.