Key message Warming will induce an upward displace-ment of Scots pine, but this can be partially mitigated by maintaining a more intense land use. & Context Scots pine is currently declining in most inner alpine sectors of southern Europe. The relative contribution of climate, land use change, and disturbances on the decline is poorly understood. What will be the future distribution of the species? Is vegetation shifting toward oak-dominated forests? What is the role of extreme drought years? & Aims The aims of the study were to determine drivers of current distribution of Scots pine and downy oak in Aosta valley (SWAlps), to extrapolate species distribution models to year 2080 (Special Report on Emissions Scenarios (SRES) A1B), and to assess the correlation between pine vitality after the extreme droughts of 2003 and 2006, and modeled longterm vegetation changes. & Methods Ensemble distribution models were created using climate, topography, soil, competition, natural disturbances, and land use. Species presence was derived from a regional forest inventory. Pine response to drought of 2003–2006 was assessed by Normalized Difference Vegetation Index (NDVI) differencing and correlated to modeled cover change between 2080 and present. & Results Scots pine and downy oak were more likely to occur under higher climatic aridity. Scots pine was also associated to higher wildfire frequency, land use intensity, and lack of competition. In a warming scenario, pine experienced an elevational displacement. This was partially counteracted if no land abandonment was hypothesized. Downy oak cover increased in all scenarios. Short-and long-term drought re-sponses of pine were unrelated. & Conclusion Warming will induce an upward displacement of pine, but this can be partially mitigated by maintaining a more intense land use. The drought-induced decline in pine vitality after extreme years did not overlap to the modeled species response under climate warming; responses to short-term drought must be more thoroughly understood in order to predict community shifts.