Latitudinal and altitudinal gradients can be utilized to forecast the impact of climate change on forests. To improve the understanding of how these gradients impact forest dynamics, we tested two hypotheses: (1) the change of the tree growth–climate relationship is similar along both latitudinal and altitudinal gradients, and (2) the time periods during which climate affects growth the most occur later towards higher latitudes and altitudes. To address this, we utilized tree-ring data from a latitudinal gradient in Finland and from two altitudinal gradients on the Tibetan Plateau. We analysed the latitudinal and altitudinal growth patterns in tree rings and investigated the growth–climate relationship of trees by correlating ring-width index chronologies with climate variables, calculating with flexible time windows, and using daily-resolution climate data. High latitude and altitude plots showed higher correlations between tree-ring chronologies and growing season temperature. However, the effects of winter temperature showed contrasting patterns for the gradients. The timing of the highest correlation with temperatures during the growing season at southern sites was approximately 1 month ahead of that at northern sites in the latitudinal gradient. In one out of two altitudinal gradients, the timing for the strongest negative correlation with temperature at low-altitude sites was ahead of treeline sites during the growing season, possibly due to differences in moisture limitation. Mean values and the standard deviation of tree-ring width increased with increasing mean July temperatures on both types of gradients. Our results showed similarities of tree growth responses to increasing seasonal temperature between latitudinal and altitudinal gradients. However, differences in climate–growth relationships were also found between gradients due to differences in other factors such as moisture conditions. Changes in the timing of the most critical climate variables demonstrated the necessity for the use of daily-resolution climate data in environmental gradient studies.