Natural aerosol components such as particulate methanesulfonic acid (MSA p) play an important role in the Arctic climate. However, numerical models struggle to reproduce MSA p concentrations and seasonality. Here we present an alternative data-driven methodology for modeling MSA p at four High Arctic stations (Alert, Gruvebadet, Pituffik (formerly Thule), and Utqiaġvik (formerly Barrow)). In our approach, we create input features that consider the ambient conditions experienced during atmospheric transport (e.g., dimethyl sulfide (DMS) emission, temperature, radiation, cloud cover, precipitation) for use in two data-driven models: a random forest (RF) regressor and an additive model (AM). The most important features were selected through automatic selection procedures, and their relationships with MSA p model output was investigated. Although the overall performance of our data-driven models on test data is modest (max. R 2 = 0.29), the models can capture variability in the data well (max. Pearson correlation coefficient = 0.77), outperform the current numerical models and reanalysis products, and produce physically interpretable results. The data-driven models selected features which can be grouped into three categories, the sources, chemical processing, and removal of MSA p , with specific differences between stations. The seasonal cycles and selected features suggest gas-phase oxidation is relatively more important during peak concentration months at Alert, Gruvebadet, and Pituffik (Thule), while aqueous-phase oxidation is relatively more important at Utqiaġvik (Barrow). Alert and Pituffik (Thule) appear to be more influenced by processes aloft than in the boundary layer. Our models usually selected chemical-processing-related features as the main factors influencing MSA p predictions, highlighting the importance of properly simulating oxidation-related processes in numerical models.