Air-quality forecasting over Europe relies largely on chemical transport models, whose computational cost limits forecast resolution and frequency. This study presents EMEPai, a regional data-driven forecasting system designed to emulate atmospheric composition fields produced by the EMEP/MSC-W modeling and data-assimilation framework. The model uses a graph-based encoder–processor–decoder architecture to generate three-hourly forecasts for NO<sub>2</sub>, O<sub>3</sub>, PM<sub>10</sub>, PM<sub>2.5</sub>, and SO<sub>2</sub> over Europe at 0.1° resolution, with lead times up to 96 hours.</p> <p>Against surface observations, its performance exceeds that of the corresponding EMEP hindcast while approaching the skill of the assimilated reanalysis. Reduced skill is found for SO<sub>2</sub> and during high-concentration events. In an operational forecast setting, EMEPai performs comparably to or better than the CAMS regional ensemble across the evaluated pollutants and forecast lead times. A 96-hour forecast requires only seconds on a single GPU, with the full forecast workflow approximately 230–460 times faster than the corresponding EMEP/MSC-W worfklow.