| Summary: | Satellite-retrieved aerosol optical depth (AOD) data are extensively integrated with ground-level measurements to achieve spatially continuous fine particulate matters (PM<sub>2.5</sub>). Current satellite-based methods however face challenges in obtaining highly accurate and reasonable PM<sub>2.5</sub> distributions due to the inability to handle both spatial non-stationarity and complex non-linearity in the PM<sub>2.5</sub>–AOD relationship. High-resolution (<1 km) PM<sub>2.5</sub> products over the whole of China for fine exposure assessment and health research are also lacking. This study aimed to predict 750 m resolution ground-level PM<sub>2.5</sub> in China with the high-resolution Visible Infrared Imaging Radiometer Suite (VIIRS) intermediate product (IP) AOD data using a newly developed geographically neural network weighted regression (GNNWR) model. The performance evaluations demonstrated that GNNWR achieved higher prediction accuracy than the widely used methods with cross-validation and predictive R<sup>2</sup> of 0.86 and 0.85. Satellite-derived monthly 750 m resolution PM<sub>2.5</sub> data in China were generated with robust prediction accuracy and almost complete coverage. The PM<sub>2.5</sub> pollution was found to be greatly improved in 2018 in China with annual mean concentration of 31.07 ± 17.52 µg/m<sup>3</sup>. Nonetheless, fine-scale PM<sub>2.5</sub> exposures at multiple administrative levels suggested that PM<sub>2.5</sub> pollution in most urban areas needed further control, especially in southern Hebei Province. This work is the first to evaluate the potential of VIIRS IP AOD in modeling high-resolution PM<sub>2.5</sub> over large-scale. The newly satellite-derived PM<sub>2.5</sub> data with high spatial resolution and high prediction accuracy at the national scale are valuable to advance environmental and health researches in China.
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