AbstractRadial tree growth is sensitive to environmental conditions, making observed growth increments an important indicator of climate change effects on forest growth. However, unprecedented climate variability could lead to non‐stationarity, that is, a decoupling of tree growth responses from climate over time, potentially inducing biases in climate reconstructions and forest growth projections. Little is known about whether and to what extent environmental conditions, species, and model type and resolution affect the occurrence and magnitude of non‐stationarity. To systematically assess potential drivers of non‐stationarity, we compiled tree‐ring width chronologies of two conifer species, Picea abies and Pinus sylvestris, distributed across cold, dry, and mixed climates. We analyzed 147 sites across the Europe including the distribution margins of these species as well as moderate sites. We calibrated four numerical models (linear vs. non‐linear, daily vs. monthly resolution) to simulate growth chronologies based on temperature and soil moisture data. Climate–growth models were tested in independent verification periods to quantify their non‐stationarity, which was assessed based on bootstrapped transfer function stability tests. The degree of non‐stationarity varied between species, site climatic conditions, and models. Chronologies of P. sylvestris showed stronger non‐stationarity compared with Picea abies stands with a high degree of stationarity. Sites with mixed climatic signals were most affected by non‐stationarity compared with sites sampled at cold and dry species distribution margins. Moreover, linear models with daily resolution exhibited greater non‐stationarity compared with monthly‐resolved non‐linear models. We conclude that non‐stationarity in climate–growth responses is a multifactorial phenomenon driven by the interaction of site climatic conditions, tree species, and methodological features of the modeling approach. Given the existence of multiple drivers and the frequent occurrence of non‐stationarity, we recommend that temporal non‐stationarity rather than stationarity should be considered as the baseline model of climate–growth response for temperate forests.