EMEPai 1.0.0: A fast regional data-driven air-quality forecast model for Europe

Abstract. 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 NO2, O3, PM10, PM2.5, and SO2 over Europe at 0.1° resolution, with lead times up to 96 hours. 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 SO2 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.