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<p>Annually resolved tree-ring records extending back to pre-industrial conditions have the potential to constrain the responses of global land surface models at interannual to centennial timescales. Here, we demonstrate a framework to simultaneously constrain the representation of tree growth and physiology in the ORCHIDEE global land surface model using the simulated variability of tree-ring width and carbon (<span class="inline-formula">Δ¹³</span>C) and oxygen (<span class="inline-formula"><i>δ</i>¹⁸</span>O) stable isotopes in six sites in boreal and temperate Europe. We exploit the resulting tree-ring triplet to derive integrative constraints for leaf physiology and growth from well-known mechanistic relationships among the variables. ORCHIDEE simulates <span class="inline-formula">Δ¹³</span>C (<span class="inline-formula"><i>r</i>=0.31</span>–0.80) and <span class="inline-formula"><i>δ</i>¹⁸</span>O (<span class="inline-formula"><i>r</i>=0.36</span>–0.74) better than tree-ring width (<span class="inline-formula"><i>r</i>&lt;0.55</span>), with an overall skill similar to that of a tree-ring model (MAIDENiso) and another isotope-enabled global vegetation model (LPX-Bern). The comparison with tree-ring data showed that growth variability is not well represented in ORCHIDEE and that the parameterization of leaf-level physiological responses (stomatal control) to drought stress in the temperate region can be constrained using the interannual variability of tree-ring stable isotopes. The representation of carbon storage and remobilization dynamics emerged as a critical process to improve the realism of simulated growth variability, temporal carryover, and recovery of forest ecosystems after climate extremes. Simulated forest gross primary productivity (GPP) correlates with simulated tree-ring <span class="inline-formula">Δ¹³</span>C and <span class="inline-formula"><i>δ</i>¹⁸</span>O variability, but the origin of the correlations with tree-ring <span class="inline-formula"><i>δ</i>¹⁸</span>O is not entirely physiological. The integration of tree-ring data and land surface models as demonstrated here should guide model improvements and contribute towards reducing current uncertainties in forest carbon and water cycling.</p>