Earth system models (ESMs) rely on the calculation of canopy conductance in land surface models (LSMs) to quantify the partitioning of land surface energy, water, andCO2fluxes. This is achieved by scaling stomatal conductance,gw, determined from physiological models developed for leaves. Traditionally, models forgwhave been semi‐empirical, combining physiological functions with empirically determined calibration constants. More recently, optimization theory has been applied to modelgwinLSMs under the premise that it has a stronger grounding in physiological theory and might ultimately lead to improved predictive accuracy. However, this premise has not been thoroughly tested. Using original field data from contrasting forest systems, we compare a widely used empirical type and a more recently developed optimization‐typegwmodel, termedBBandMED, respectively. Overall, we find no difference between the two models when used to simulategwfrom photosynthesis data, or leaf gas exchange from a coupled photosynthesis‐conductance model, or gross primary productivity and evapotranspiration for aFLUXNETtower site with theCLM5 communityLSM. Field measurements reveal that the key fitted parameters forBBandMED,g1Bandg1M,exhibit strong species specificity in magnitude and sensitivity toCO2, andCLM5 simulations reveal that failure to include this sensitivity can result in significant overestimates of evapotranspiration for high‐CO2scenarios. Further, we show thatg1Bandg1Mcan be determined from meanci/ca(ratio of leaf intercellular to ambientCO2concentration). Applying this relationship withci/cavalues derived from a leaf δ13C database, we obtain a global distribution ofg1Bandg1M, and these values correlate significantly with mean annual precipitation. This provides a new methodology for global parameterization of theBBandMEDmodels inLSMs, tied directly to leaf physiology but unconstrained by spatial boundaries separating designated biomes or plant functional types.