Improved GPP Modeling Reveals Rising Precipitation Influence in Typical Grasslands of Northern China’s Hulun Lake Basin

The Hulun Lake Basin in northern China harbors extensive temperate grasslands, whose carbon uptake and ecosystem functioning are effectively represented by gross primary productivity (GPP). However, the long-term response of grassland GPP to regional climate change remains insufficiently understood, due to the limited ability of global models to capture local ecosystem variability. This study improved GPP estimation in the Hulun Lake Basin by integrating additional grassland flux observations from China into a Random Forest model (MCF). We subsequently examined the spatiotemporal dynamics of GPP and identified the dominant climatic drivers and their long-term trends under changing climate conditions. Results show that the MCF model significantly outperformed the model trained solely on FLUXNET 2015 Tier 2 data in both accuracy and trend patterns of grassland GPP. A 23-year MCF simulation in the Hulun Lake Basin showed a clear west–east GPP gradient, averaging 676.36 g C m− 2 yr− 1 and totaling 70.13 Tg C yr− 1. Most of the region exhibited increasing GPP trends, with an average growth rate of 6.85 g C m− 2 yr− 1, resulting in an annual total increase of approximately 0.45 Tg C yr− 1. SHAP analysis demonstrated that shifts in precipitation and temperature are reshaping the dominant factors contributing to GPP increases. Structural equation modeling further revealed that precipitation enhanced GPP through both direct and indirect pathways, with the direct effect accounting for 63.6