Abstract Tree regeneration is a key demographic process influencing long‐term forest dynamics. It is driven by climate, disturbances, biotic factors and their interactions. Thus, predictions of tree regeneration are challenging due to complex feedbacks along the wide climatic gradients covered by most tree species. The stress gradient hypothesis (SGH) provides a framework for assessing such feedbacks across species ranges, suggesting that competition between trees is more frequent under favourable conditions, whereas reduced competition (i.e. positive interactions) is more likely under climatic stress. Moreover, tree life‐history strategies (LHS) may shed light on how and whether the SGH explains regeneration of different tree species. To address these topics, we developed statistical models based on >50,000 recruitment events observed for 24 tree species in an extensive permanent plot network (6540 plots from 299 unmanaged European temperate, boreal and subalpine forests) covering a wide climatic gradient. We found that the effects of Leaf Area Index (as a proxy for competition) on tree recruitment changed along climatic gradients but in a species‐specific manner. Competition predominates, with its intensity decreasing under stressful conditions for most species, as predicted by the SGH. However, positive interactions were only evident for a few species. Additionally, the ability of the SGH to explain patterns of competition and positive interactions across the gradients differed among species, with some differences and exceptions that may be related to varying LHS. Synthesis. Our study shows that competition between trees toward climatic stress decreases systematically but depends on species stress tolerance to climate and shade. These findings explain within‐ and between‐species differences in tree recruitment patterns in European temperate forests. Moreover, our findings imply that projections of forest dynamics along wide climatic gradients and under climate change must accommodate both competition and positive interactions, as they strongly affect rates of community turnover.