Forests are a substantial terrestrial carbon sink, but anthropogenic changes in land use and climate have considerably reduced the scale of this system¹. Remote-sensing estimates to quantify carbon losses from global forests^2–5 are characterized by considerable uncertainty and we lack a comprehensive ground-sourced evaluation to benchmark these estimates. Here we combine several ground-sourced⁶ and satellite-derived approaches^2,7,8 to evaluate the scale of the global forest carbon potential outside agricultural and urban lands. Despite regional variation, the predictions demonstrated remarkable consistency at a global scale, with only a 12% difference between the ground-sourced and satellite-derived estimates. At present, global forest carbon storage is markedly under the natural potential, with a total deficit of 226 Gt (model range = 151–363 Gt) in areas with low human footprint. Most (61%, 139 Gt C) of this potential is in areas with existing forests, in which ecosystem protection can allow forests to recover to maturity. The remaining 39% (87 Gt C) of potential lies in regions in which forests have been removed or fragmented. Although forests cannot be a substitute for emissions reductions, our results support the idea^2,3,9 that the conservation, restoration and sustainable management of diverse forests offer valuable contributions to meeting global climate and biodiversity targets.

Climate-smart restoration and sustainable ecosystem management requires understanding which plant species will survive and thrive under both current and future climate conditions. Changing climate conditions are expected to place new environmental stresses on plants across biomes, potentially shifting the climate envelope, with unexpected consequences for ecological communities. Here, we train machine learning models on the global distribution of Earth’s biomes, demonstrating that soil and climate can accurately capture contemporary biome-climate envelopes (BCEs) with 90% accuracy. We then predict how changes in climate might alter BCEs under different climate change scenarios (RCP 4.5 and 8.5). We find that 11–17% of the terrestrial surface is likely to experience a change in BCE by 2080 and 16–19% has a highly uncertain BCE trajectory over the coming decades. BCE boundaries generally shift poleward, with the tundra BCE shrinking by up to 47% under RCP 8.5, and various dryland BCEs overall increasing under RCP 8.5. These models do not necessarily reflect changes in vegetation characteristics under future climate conditions, as uncertainty about ecological feedbacks and migration rates preclude our capacity to predict how climate variation would impact these vegetation dynamics. However, we identify regions where changing climate conditions might make the local environments stressful for the vegetation that presently exists in those regions.