Abstract Synchronization of terrestrial and riverine gross primary productivity (GPP) reflects seasonal coordination of carbon uptake across land–water interfaces. Although watershed processes link terrestrial and aquatic ecosystems, the environmental influences on their productivity synchronization remain insufficiently quantified. To address this, we analyzed coupling strength (CS), defined as the annual correlation between daily terrestrial and riverine GPP, across 142 U.S. watersheds. Using interpretable machine learning, we found that water temperature, reach width, watershed area, and terrestrial leaf area index were the strongest predictors of CS, with non‐linear, context‐dependent effects and interactions among factors. Clustering analysis based on interpreted predictor effects revealed four distinct coupling regimes, reflecting contrasting combinations of watershed attributes such as climate, land cover, and anthropogenic impacts. Our study provides a robust and interpretable framework for evaluating productivity alignment across terrestrial–riverine ecosystems, highlighting the diverse local environmental influences on continental‐scale watershed carbon dynamics.

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