Ambient fine particulate matter (PM_2.5) is the leading global environmental determinant of mortality. However, large gaps exist in ground-based PM_2.5monitoring. Satellite remote sensing of aerosol optical depth (AOD) offers information to fill these gaps worldwide, when augmented with a modeled PM_2.5 to AOD relationship (η). This study aims to understand the spatial pattern and driving factors of η from both observations and modeling. A global observational estimate of η for the year 2019 is inferred from 6,118 ground-based PM_2.5 measurement sites and satellite retrieved AOD from the MAIAC algorithm. A global chemical transport model, GEOS-Chem, in its high performance configuration (GCHP), is used to interpret the observed spatial pattern of annual mean η. Measurements and the GCHP simulation consistently identify a global population-weighted mean η of 92 – 100 μg/m³, with regional values ranging from 60.3 μg/m³for North America to more than 130 μg/m³ in Africa. The highest η is found in arid regions where aerosols are less hygroscopic due to mineral dust, followed by regions strongly influenced by surface aerosol sources. Relatively low η is found over regions distant from strong aerosol sources. The spatial variation of η is strongly influenced by aerosol composition driven by its effects on aerosol hygroscopicity. Sensitivity tests with globally uniform parameters reveal that aerosol composition leads to the strongest η spatial variability, with a population-weighted normalized mean difference of 12.3 μg/m³, higher than that from aerosol vertical profile (8.4 μg/m³), reflecting the determinant composition effects on aerosol hygroscopicity and aerosol optical properties.