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Long-term surface NO₂ data are essential for retrospective policy evaluation and chronic human exposure assessment. In the absence of NO₂ observations for Mainland China before 2013, training a model with 2013–2018 data to make predictions for 2005–2012 (back-extrapolation) could cause substantial estimation bias due to concept drift.

This study aims to correct the estimation bias in order to reconstruct the spatiotemporal distribution of daily surface NO₂ levels across China during 2005–2018.

On the basis of ground- and satellite-based data, we proposed the robust back-extrapolation with a random forest (RBE-RF) to simulate the surface NO₂ through intermediate modeling of the scaling factors. For comparison purposes, we also employed a random forest (Base-RF), as a representative of the commonly used approach, to directly model the surface NO₂ levels.

The …