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• For the first time, GC×GC-TOFMS was used to classify strong aroma-type baijiu (SAB) from different regions. • The SAB samples from two different basins in China were clearly separated in PCA score plots. • The predictive ability of the PLS-DA model was excellent, correctly classifying 100% of the unknown SAB samples. • Twenty-three potential markers were identified using standard compounds. • The HCA result showed that the selected markers offered strong discrimination power. A metabolomics strategy was developed to differentiate strong aroma-type baijiu (SAB) (distilled liquor) from the Sichuan basin (SCB) and Yangtze-Huaihe River Basin (YHRB) through liquid–liquid extraction coupled with GC×GC-TOFMS. PCA effectively separated the samples from these two regions. The PLS-DA training model was excellent, with explained variation and predictive capability values of 0.988 and 0.982, respectively. As a result, the model demonstrated its ability to perfectly differentiate all the unknown SAB samples. Twenty-nine potential markers were located by variable importance in projection values, and twenty-four of them were identified and quantitated. Discrimination ability is closely correlated to the characteristic flavor compounds, such as acid, esters, furans, alcohols, sulfides and pyrazine. Most of the marker compounds were less abundant in the SCB samples than in the YHRB samples. The quantitated markers were further processed using hierarchical cluster analysis for targeted analysis, indicating that the markers had great discrimination power to differentiate the SAB samples. [ABSTRACT FROM AUTHOR]