Climate change is increasingly affecting plant species distributions, in ways that need to be predicted. Here, in a novel prediction approach, we developed the relevant climate niche (RCN) of plants, based on thorough selection of climate variables and implementation of a non-parametric Bayesian network for climate simulations. The RCN was conditionalized to project the fate of Silene acaulis in North America under moderate (Representative Concentration Pathway 4.5; RCP4.5) and extreme(RCP8.5) short-term (2011-2040) climate scenarios. We identified a three-variable climate hypervolume for S. acaulis. Within 20 years >50% of current locations of the species will be outside the defined climate hypervolume. It could compensate for climate change in 2011-2040 through a poleward shift of 0.97 degrees C or an upshift of 138 m in the RCP4.5 scenario, and 1.29 degrees C or 184 m in the RCP8.5 scenario. These results demonstrate the benefits of redefining the climate niche of plant species in the form of a user-defined, data-validated, hierarchical network comprising only variables that are consistent with species distribution. Advantages include realism and interpretability in niche modeling, and new opportunities for predicting future species distributions under climate change. (C) 2019 Elsevier B.V. All rights reserved.