Gradient Forests is a machine learning algorithm that is gaining in popularity for studying the environmental drivers of genomic variation and for incorporating genomic information into climate change impact assessments. Here we provide the first experimental evaluation of the ability of ‘genomic offsets’ - a metric of climate maladaptation derived from Gradient Forests - to predict organismal responses to environmental change. We used high-throughput sequencing, genome scans, and several methods (including Gradient Forests) to identify candidate loci associated with climate adaptation in balsam poplar (Populus balsamifera L.). Individuals collected throughout balsam poplar’s range also were planted in two common garden experiments. We used Gradient Forests to relate candidate loci to environmental gradients and to predict the expected magnitude of response (i.e., the genetic offset) of populations when transplanted from their “home” environment to the new environments in the common gardens. We then compared the predicted genetic offsets to measurements of population performance in the common gardens. We found the expected inverse relationship between genetic offset and performance in the common gardens: populations with larger predicted genetic offsets performed worse in the common gardens than populations with smaller offsets. Also, genetic offset better predicted performance in the common gardens than did ‘naive’ climate distances. Our results provide preliminary evidence that genomic offsets may provide a first order estimate of the degree of expected maladaptation of populations exposed to rapid environmental change.