The rising global energy demand and the need to mitigate climate change have driven the search for sustainable energy solutions, with offshore wind energy emerging as a key component. With its vast coastline, India recognizes offshore wind energy as essential to its renewable energy strategy. However, the availability of wind resources is highly sensitive to climate change, making it critical to assess the potential impacts of future climatic shifts on offshore wind energy. This study employs the Multi-Model and Multi-Method Ensemble (4ME) framework to refine climate model projections using statistical downscaling techniques, providing high-resolution wind resource assessments for India’s offshore regions. A total of 13 Global Climate Models (GCMs) from the CMIP6 dataset were selected, and five downscaling methods were applied to generate refined wind speed projections from 1990–2014. The performance of individual model-method combinations was evaluated using statistical metrics, and the most accurate combinations were integrated into the 4ME ensemble. The results demonstrate the superiority of the 4ME ensemble over individual GCM projections, offering more reliable and consistent wind resource estimates. Spatial bias analysis reveals the effectiveness of the 4ME framework in reducing model discrepancies and improving alignment with ERA5 reanalysis data, particularly in offshore regions. This study provides valuable insights into future wind energy potential and underscores the importance of ensemble-based frameworks for more accurate climate impact assessments in energy planning.