GEOS-Chem, a chemical transport model, provides time-space continuous estimates of atmospheric pollutants including PM2.5 and its major components, but model predictions are not highly correlated with ground monitoring data. In addition, its spatial resolution is usually too coarse to characterize the spatial pattern in pollutant concentrations in urban environments. Our objective was to calibrate daily GEOS-Chem simulations using ground monitoring data and incorporating meteorological variables, land-use terms and spatial-temporal lagged terms. Major PM2.5 components of our interest include sulfate, nitrate, organic carbon, elemental carbon, ammonium, sea salt and dust. We used a backward propagation neural network to calibrate GEOS-Chem predictions with a spatial resolution of 0.500° × 0.667° using monitoring data collected during the period from 2001 to 2010 for the Northeastern United States. Subsequently, we made predictions at 1 km × 1 km grid cells. We determined the accuracy of the spatial-temporal predictions using ten-fold cross-validation and “leave-one-day-out” cross-validation techniques. We found a high total R2 for PM2.5 mass (all data R2 0.85, yearly values: 0.80–0.88) and PM2.5 components (R2 for individual components were around 0.70–0.80). Our model makes it possible to assess spatially- and temporally-resolved short- and long-term exposures to PM2.5 mass and components for epidemiological studies. •A hybrid model is developed for total PM2.5 and PM2.5 chemical components.•Neural network is used to handle complexity in modeling.•Model results are available at 1 km × 1 km grid cell at daily basis.•Model outcome demonstrates high correlation with monitoring data.