<p><strong>Abstract.</strong> CO₂ production in soils responds strongly to changes in temperature and moisture, but the magnitude of such responses at different timescales remains difficult to predict. Knowledge of the mechanisms leading to the often observed interactions in the effects of these drivers on soil CO₂ emissions is especially limited. Here we test the ability of different soil carbon models to simulate responses measured in soils incubated at a range of

more »

... e levels and cycled through 5, 20, and 35<span class="thinspace"></span>°C. We applied parameter optimization methods while modifying two structural components of models: (1) the reaction kinetics of decomposition and uptake and (2) the functions relating fluxes to soil moisture. We found that the observed interactive patterns were best simulated by a model using Michaelis–Menten decomposition kinetics combined with diffusion of dissolved carbon (C) and enzymes. In contrast, conventional empirical functions that scale decomposition rates directly were unable to properly simulate the main observed interactions. Our best model was able to explain 87<span class="thinspace"></span>% of the variation in the data. Model simulations revealed a central role of Michaelis–Menten kinetics as a driver of temperature sensitivity variations as well as a decoupling of decomposition and respiration C fluxes in the short and mid-term, with general sensitivities to temperature and moisture being more pronounced for respiration. Sensitivity to different model parameters was highest for those affecting diffusion limitations, followed by activation energies, the Michaelis–Menten constant, and carbon use efficiency. Testing against independent data strongly validated the model (<i>R</i>² = 0.99) and highlighted the importance of initial soil C pool conditions. Our results demonstrate the importance of model structure and the central role of diffusion and reaction kinetics for simulating and understanding complex dynamics in soil C.</p>