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<p>Fine particulate matter with aerodynamic diameters <span class="inline-formula">≤2.5</span>&thinsp;<span class="inline-formula">µ</span>m (PM<span class="inline-formula">_2.5</span>) has adverse effects on human health and the atmospheric environment. The estimation of surface PM<span class="inline-formula">_2.5</span> concentrations has made intensive use of satellite-derived aerosol products. However, it has been a great challenge to obtain high-quality and high-resolution PM<span class="inline-formula">_2.5</span> data from both ground and satellite observations, which is essential to monitor air pollution over small-scale areas such as metropolitan regions. Here, the space–time extremely randomized trees (STET) model was enhanced by integrating updated spatiotemporal information and additional auxiliary data to improve the spatial resolution and overall accuracy of PM<span class="inline-formula">_2.5</span> estimates across China. To this end, the newly released Moderate Resolution Imaging Spectroradiometer Multi-Angle Implementation of Atmospheric Correction AOD product, along with meteorological, topographical and land-use data and pollution emissions, was input to the STET model, and daily 1&thinsp;km PM<span class="inline-formula">_2.5</span> maps for 2018 covering mainland China were produced. The STET model performed well, with a high out-of-sample (out-of-station) cross-validation coefficient of determination (<span class="inline-formula"><i>R</i>²</span>) of 0.89 (0.88), a low root-mean-square error of 10.33 (10.93)&thinsp;<span class="inline-formula">µ</span>g&thinsp;m<span class="inline-formula">⁻³</span>, a small mean absolute error of 6.69 (7.15)&thinsp;<span class="inline-formula">µ</span>g&thinsp;m<span class="inline-formula">⁻³</span> and a small mean relative error of 21.28&thinsp;% (23.69&thinsp;%). In particular, the model captured well the PM<span class="inline-formula">_2.5</span> concentrations at both regional and individual site scales. The North China Plain, the Sichuan Basin and Xinjiang Province always featured high PM<span class="inline-formula">_2.5</span> pollution levels, especially in winter. The STET model outperformed most models presented in previous related studies, with a strong predictive power (e.g., monthly <span class="inline-formula"><i>R</i>²=0.80</span>), which can be used to estimate historical PM<span class="inline-formula">_2.5</span> records. More importantly, this study provides a new approach for obtaining high-resolution and high-quality PM<span class="inline-formula">_2.5</span> dataset across mainland China (i.e., ChinaHighPM<span class="inline-formula">_2.5</span>), important for air pollution studies focused on urban areas.</p>