Global climate-model projections are often too coarse to represent the spatial variability required for regional assessments. This study presents CMIP6-MedPlus, an open-access climate projection dataset for an extended Mediterranean region, available at <a href="https://doi.org/10.5281/zenodo.17898529" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.17898529</a> (Todaro et al., 2025). It provides daily precipitation and near-surface air temperature at 0.25° spatial resolution for the period 1985–2100, based on five Coupled Model Intercomparison Project Phase 6 (CMIP6) Global Climate Models (GCMs) and two Shared Socioeconomic Pathways (SSP1-2.6 and SSP3-7.0). CMIP6-MedPlus was generated using a statistical downscaling framework that combines deep learning–based spatial refinement with bias correction. First, a Convolutional Neural Network (CNN)-based method was applied to enhance spatial resolution: ERA5 reanalysis fields were upscaled to the GCM resolution and used to train the CNN to learn the mapping between coarse- and fine-scale representations of the same variable. The trained CNN was then applied to GCM outputs to generate more spatially detailed fields. Second, quantile delta mapping was applied to the CNN-refined fields to correct systematic biases relative to ERA5 while preserving climate change signals. The resulting dataset provides spatially consistent regional climate information suitable for climate-impact across Mediterranean and neighbouring areas and serves as a basis for subsequent refinement at basin and local scales.