Two centuries of studies have demonstrated the importance of understanding the interaction between air temperature and carbon dioxide (CO₂) emissions, which can impact the climate system and human life in various ways, and across different timescales. While historical interactions have been consistently studied, the nature of future interactions and the impacts of confounding factors still require more investigation in keeping with the continuous updates of climate projections to the end of the 21st century. Phase 6 of the Coupled Model Intercomparison Project (CMIP6), like its earlier projects, provides ScenarioMIP multi-model projections to assess the climate under different radiative forcings ranging from a low-end (SSP1–2.6) to a high-end (SSP5–8.5) pathway. In this study, we analyze the localized causal structure of CO_2, and near-surface mean air temperature (meanT) interaction for four scenarios from three CMIP6 models using a rigorous multivariate information flow (IF) causality,which can separate the cause from the effect within the interaction (CO ₂–meanT and meanT–CO₂) by measuring the rate of IF between parameters. First, we obtain patterns of the CO₂ and meanT causal structures over space and time. We found a contrasting emission-based impact of soil moisture (SM) and vegetation (leaf area index (LAI)) changes on the meanT–CO₂ causal patterns. That is, SM influenced CO₂ sink regions in SSP1–2.6 and source regions in SSP5–8.5, and vice versa found for LAI influences. On the other hand, they function similarly to constrain the future CO₂ impact on meanT. These findings are essential for improving long-term predictability where climate models might be limited.