Simulations of biomass burning (BB) emissions in global chemistry and aerosol transport models depend on external inventories, which provide location and strength for BB aerosol sources. Our previous work shows that to first order, satellite snapshots of aerosol optical depth (AOD) near the emitted smoke plume can be used to constrain model‐simulated AOD, and effectively, the smoke source strength. We now refine the satellite‐snapshot method and investigate where applying simple multiplicative emission adjustment factors alone to the widely used Global Fire Emission Database version 3 emission inventory can achieve regional‐scale consistency between Moderate Resolution Imaging Spectroradiometer (MODIS) AOD snapshots and the Goddard Chemistry Aerosol Radiation and Transport model. The model and satellite AOD are compared globally, over a set of BB cases observed by the MODIS instrument during the 2004, and 2006-2008 biomass burning seasons. Regional discrepancies between the model and satellite are diverse around the globe yet quite consistent within most ecosystems. We refine our approach to address physically based limitations of our earlier work (1) by expanding the number of fire cases from 124 to almost 900, (2) by using scaled reanalysis‐model simulations to fill missing AOD retrievals in the MODIS observations, (3) by distinguishing the BB components of the total aerosol load from background aerosol in the near‐source regions, and (4) by including emissions from fires too small to be identified explicitly in the satellite observations. The small‐fire emission adjustment shows the complimentary nature of correcting for source strength and adding geographically distinct missing sources. Our analysis indicates that the method works best for fire cases where the BB fraction of total AOD is high, primarily evergreen or deciduous forests. In heavily polluted or agricultural burning regions, where smoke and background AOD values tend to be comparable, this approach encounters large uncertainties, and in some regions, other model‐ or measurement‐related factors might contribute significantly to model‐satellite discrepancies. This work sets the stage for a larger study within the Aerosol Comparison between Observations and Models (AeroCOM) multimodel biomass burning experiment. By comparing multiple model results using the refined technique presented here, we aim to separate BB inventory from model‐specific contributions to the remaining discrepancies.