To support climate-resilient forest planning across Europe, we are developing high-resolution suitability maps for 50 common tree species under future climate scenarios. The approach builds on a harmonized presence–absence dataset derived from over 270,000 National Forest Inventory (NFI) plots from 11 countries, complemented with publicly available records to ensure broad spatial coverage. Species–climate relationships are modeled using a suite of machine learning algorithms trained on historical climatologies and projected using bias-corrected outputs from five GCM–RCM chains within the EUR-11 domain, under RCP4.5 and RCP8.5. The modeling pipeline is designed to produce decadal projections at 1 km spatial resolution, allowing fine-scale exploration of ecological suitability from 2030 to 2100. To enhance predictive robustness, multiple algorithms are combined through ensemble methods, including stacking, using a limited set of ecologically relevant predictors. This work complements existing efforts in species distribution modeling by integrating high spatial and temporal granularity with a multi-model climate ensemble and a harmonized pan-European observational dataset. The resulting maps will be integrated into the EU reforestation planner tool, supporting long-term, spatially explicit strategies for tree species selection under changing climatic conditions.