Global warming threatens the productivity of forest plantations. We propose here the integration of environmental information into a genomic evaluation scheme using individual reaction norms, to enable the quantification of resilience in forest tree improvement and conservation strategies in the coming decades. Random regression models were used to fit wood ring series, reflecting the longitudinal phenotypic plasticity of tree growth, according to various environmental gradients. The predictive ability of the models was considered to select the most relevant environmental gradient, namely a gradient derived from an ecophysiological model and combining trunk water potential and temperature. Even if the individual ranking was preserved over most of the environmental gradient, strong genotype x environment interactions were detected in the extreme unfavorable part of the gradient, which includes environmental conditions that are very likely to be more frequent in the future. Combining genomic information and longitudinal data allowed to predict the growth of individuals in environments where they have not been observed. Phenotyping of 50% of the individuals in all the environments studied allowed to predict the growth of the remaining 50% of individuals in all these environments with a predictive ability of 0.25. Without changing the total number of observations, adding observations in a reduced number of environments for the individuals to be predicted, while decreasing the number of individuals phenotyped in all environments, increased the predictive ability to 0.59, highlighting the importance of phenotypic data allocation. We found that genomic reaction norms are useful for the characterization and prediction of the function of genetic parameters and facilitate breeding in a climate change context.Read less <