This research reveals new insights into the weather drivers of land surface phenology (LSP) across the entire European forest, while at the same time it establishes a new conceptual framework for modelling LSP. Specifically, a sophisticated machine learning regression method (RF) was introduced for LSP modelling across very large areas and across multiple years simultaneously. The RF models explained 81 and 62 % of the variance in the spring and autumn LSP interannual variation.