Spatiotemporal resolved models were developed predicting daily fine particulate matter (PM₂.₅) concentrations across Switzerland from 2003 to 2013. Relatively sparse PM₂.₅ monitoring data was supplemented by imputing PM₂.₅ concentrations at PM₁₀ sites, using PM₂.₅/PM₁₀ ratios at co-located sites. Daily PM₂.₅ concentrations were first estimated at a 1 × 1km resolution across Switzerland, using Multiangle Implementation of Atmospheric Correction (MAIAC) spectral aerosol optical depth (AOD) data in combination with spatiotemporal predictor data in a four stage approach. Mixed effect models (1) were used to predict PM₂.₅ in cells with AOD but without PM₂.₅ measurements (2). A generalized additive mixed model with spatial smoothing was applied to generate grid cell predictions for those grid cells where AOD was missing (3). Finally, local PM₂.₅ predictions were estimated at each monitoring site by regressing the residuals from the 1 × 1km estimate against local spatial and temporal variables using machine learning techniques (4) and adding them to the stage 3 global estimates. The global (1 km) and local (100 m) models explained on average 73% of the total,71% of the spatial and 75% of the temporal variation (all cross validated) globally and on average 89% (total) 95% (spatial) and 88% (temporal) of the variation locally in measured PM₂.₅ concentrations.