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Accurately identifying ecosystem-service (ES) responses to future climate change is essential for improving ecological conservation and restoration and for developing targeted adaptation strategies. However, coherent future response zones across multiple regulating services and their associated predictor patterns remain insufficiently understood. In the current study, we established a comprehensive framework based on Shared Socioeconomic Pathway-Representative Concentration Pathway (SSP-RCP) climate scenarios, by using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model assessments, hotspot analyses, as well as random forest-Shapley additive explanations (RF-SHAP) interpretable machine learning. We assessed water yield, soil conservation, nitrogen retention, and phosphorus retention responses under future SSP-RCP scenarios across China during 2050–2060 relative to a 2000–2020 baseline. Our results revealed pronounced spatial heterogeneity in China's future ESs. At the provincial scale, the unweighted mean values of water yield and soil conservation increased by 18.1–19.5% and 22.0–25.9%, whereas nitrogen and phosphorus retention decreased by 14.9–18.3% and 11.0–15.7%. Northwestern China formed a consistent positive-response zone for water yield and soil conservation. Conversely, central-eastern China formed a contiguous zone of declining phosphorus retention, while Southern China exhibited minimal response. Strong synergistic coupling was observed among the four ESs, with water-related services displaying highly consistent spatial correlations. RF-SHAP analysis indicated relatively large contributions of climate-change and hydroclimatic predictors within the selected predictor framework for most ES responses. In contrast, nitrogen retention showed broader associations with climatic, topographic, and human-activity conditions. These findings provide a scientific basis for spatially differentiated ecological conservation and climate-resilient governance.
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Tu et al. (2026) studied this question.
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