AbstractDespite the recent development of using satellite remote sensing to predict surface NO2levels in China, methods for estimating reliable historical NO2exposure, especially before the establishment of NO2monitoring network in 2013, are still rare. A gap‐filling model was first adopted to impute the missing NO2column densities from satellite, then an ensemble machine learning model incorporating three base learners was developed to estimate the spatiotemporal pattern of monthly mean NO2concentrations at 0.05° spatial resolution from 2005 to 2020 in China. Further, we applied the exposure data set with epidemiologically derived exposure response relations to estimate the annual NO2associated mortality burdens in China. The coverage of satellite NO2column densities increased from 46.9% to 100% after gap‐filling. The ensemble model predictions had good agreement with observations, and the sample‐based, temporal and spatial cross‐validation (CV)R2were 0.88, 0.82, and 0.73, respectively. In addition, our model can provide accurate historical NO2concentrations, with both by‐year CVR2and external separate year validationR2achieving 0.80. The estimated national NO2levels showed a increasing trend during 2005–2011, then decreased gradually until 2020, especially in 2012–2015. The estimated annual mortality burden attributable to long‐term NO2exposure ranged from 305 thousand to 416 thousand, and varied considerably across provinces in China. This satellite‐based ensemble model could provide reliable long‐term NO2predictions at a high spatial resolution with complete coverage for environmental and epidemiological studies in China. Our results also highlighted the heavy disease burden by NO2and call for more targeted policies to reduce the emission of nitrogen oxides in China.