Time‐Aware UNet and Super‐Resolution Deep Residual Networks for Spatial Downscaling

Satellite observations of atmospheric pollutants are often available only at coarse spatial resolution, which limits their use in local‐scale environmental analysis. Spatial downscaling methods aim to transform such data into high‐resolution fields. In this work, two widely used deep learning architectures—the super‐resolution deep residual network (SRDRN) and the encoder–decoder‐based UNet—for spatial downscaling, are extended with a lightweight temporal module that encodes observation time using either sinusoidal or radial basis function representations and integrates temporal features with spatial information. The proposed time‐aware extensions are evaluated in a case study on ozone downscaling over Italy. Results show that, with only a minor increase in computational cost, incorporating temporal information significantly improves downscaling accuracy and accelerates model convergence.