Offshore wind energy has the potential to significantly alter the energy mix of a nation. However, like other renewable energy sources, the power generated from offshore wind farms is susceptible to the impacts of climate change. Therefore, conducting critical assessments before installing offshore wind farms is crucial. Climate model data are proven valuable for evaluating offshore wind potential at specific sites or regions and assessing their susceptibility to climate change. The accuracy of these evaluations, however, depends on the biases presented in the climate models. This study aims to develop an effective ensemble approach to more accurately assess India's offshore wind potential. Two types of multi-model ensembles were created using ten CMIP6 global circulation models (GCMs). One ensemble, termed the differential weighted ensemble, assigns different weights to the GCMs. These weights are computed using various multi-criteria decision-making techniques. The other ensemble assumes equal weights for all GCMs. These ensembles were compared against data from the ECMWF reanalysis v5 (ERA5). The results indicate that the differential weighted ensemble outperforms the uniform weighted ensemble with higher overlapping percent-age, lower bias, and better K-S test results, making it a more reliable approach for offshore wind resource assessments and climate change studies.