AbstractClimate change is projected to increase the frequency and severity of droughts, possibly causing sudden and elevated tree mortality. Better understanding and predictions of boreal forest responses to climate change are needed to efficiently adapt forest management. We used tree‐ring width chronologies from the Swedish National Forest Inventory, sampled between 2010 and 2018, and a random forest machine‐learning algorithm to identify the tree, stand, and site variables that determine drought damage risk, and to predict their future spatial–temporal evolution. The dataset consisted of 16,455 cores of Norway spruce, Scots pine, and birch trees from all over Sweden. The risk of drought damage was calculated as the probability of growth anomaly occurrence caused by past drought events during 1960–2010. We used the block cross‐validation method to compute model predictions for drought damage risk under current climate and climate predicted for 2040–2070 under the RCP.2.6, RCP.4.5, and RCP.8.5 emission scenarios. We found local climatic variables to be the most important predictors, although stand competition also affects drought damage risk. Norway spruce is currently the most susceptible species to drought in southern Sweden. This species currently faces high vulnerability in 28% of the country and future increases in spring temperatures would greatly increase this area to almost half of the total area of Sweden. Warmer annual temperatures will also increase the current forested area where birch suffers from drought, especially in northern and central Sweden. In contrast, for Scots pine, drought damage coincided with cold winter and early‐spring temperatures. Consequently, the current area with high drought damage risk would decrease in a future warmer climate for Scots pine. We suggest active selection of tree species, promoting the right species mixtures and thinning to reduce tree competition as promising strategies for adapting boreal forests to future droughts.