<p>The ecosystem services provided by grasslands depend on their biomass or leaf area index (LAI). Under the background of climate change, the impact of preseason climate and extreme weather events, such as high temperature and drought, on the spatiotemporal dynamics of grassland LAI remains unclear. Here, we constructed three interpretable machine learning models (including a Bayesian model, an interpretable neural network model, and a random forest model), to investigate the impact mechanisms of climate factors on grassland LAI in China from 2001 to 2020. The results showed that all three models performed well in simulating LAI (with R² ranging from 0.540 to 0.963). The random forest model performed the best. Preseason climate was the most important factor driving LAI changes. The increase in preseason temperature, precipitation, and radiation could lead to an increase in grassland LAI. Regarding extreme weather events, heat events and heavy-rainfall events had positive effects on LAI, while frost events and no-rainfall events had negative impacts. CO₂ showed a significant fertilization effect. Grazing intensity had a relatively small impact on LAI. The impact of precipitation on LAI was greater in spring than in autumn, whereas the impacts of temperature and radiation were greater in autumn than in spring. This study develops a climate change-adaptive framework for predicting grassland growth dynamics based on machine learning models, which can provide scientific support for grassland ecological protection and adaptive management.</p>