Satellite MODIS Aerosol Optical Depth (AOD) products based on Dark Target (DT) retrieval algorithm at 3 km resolution have been widely used in the ground air pollution monitoring. However, due to the limitations of DT retrieval algorithm, these products missed a large number of pixels with low spatiotemporal coverage and limited accuracy. By contrast, MODIS 10 km DT_DB_Combined AOD products integrate products based on DT and Deep Blue (DB) retrieval algorithms. However, to some extent, DT_DB_Combined AOD products can make up for the coverage and accuracy shortcomings of MODIS 3 km DT AOD data products, the resolution of which is low. Moreover, affected by the seasonal variation of aerosol component sources and the seasonal error of surface reflectance estimation, the accuracy of MODIS AOD data products also exhibits seasonal variation. This study takes Beijing‐Tianjin‐Hebei region as the experimental area and uses MODIS 10 km DT_DB_Combined AOD products as the material. Geostatistical Inverse Model (GIM) downscaling method, which considers the spatial covariance function differences of AOD data products at different scales, is introduced. At the same time, to consider the seasonal variation characteristics of AOD, the seasonal bias correction model for the MODIS 10 km DT_DB_Combined AOD products using accurate AERONET monitoring data is developed. On this basis, the Bias-corrected GIM (BGIM) downscaling algorithm coupled with seasonal bias correction model is further proposed. The AERONET ground observation data and MODIS 3 km DT AOD products are employed as the absolute and relative evaluation reference for the BGIM downscaling results. Results show that the absolute evaluated accuracies of the downscaled MODIS 3 km DT_DB_Combined AOD data, 10 km DT_DB_Combined AOD, and 3 km DT AOD data products are relatively close; the corresponding R² values are 0.79, 0.70, and 0.71, respectively. Compared with MODIS 3 km DT AOD products, the relative evaluated R result of the seasonal corrected MODIS 3 km DT_DB_Combined AOD data is higher than 0.93. In addition, the temporal coverage and spatial coverage are increased by 11.21% and 11.44%, respectively. The spatial coverage in spring and winter is relatively higher among four seasons. The results confirm that the BGIM downscaling algorithm can not only effectively improve the resolution and accuracy of MODIS 10 km DT_DB_Combined AOD products, but also promote the spatiotemporal coverage compared with MODIS 3 km AOD products.

remote sensing;AOD;downscaling;BGIM;spatial-temporal statistics;Beijing‐Tianjin‐Hebei