Under natural conditions, plants are subjected to continuous changes of irradiance that drive variations of stomatal conductance to water vapour (gs ). We propose a dynamic model to predict the temporal response of gs at the leaf level using an asymmetric sigmoid function with a unique parameter describing time constants for increasing and decreasing gs . The model parameters were adjusted to observed data using Approximate Bayesian Computation. We tested the model performance for (1) instant changes of irradiance; or (2) continuous and controlled variations of irradiance simulating diurnal time courses. Compared with the two mostly used steady-state models, our dynamic model described daily time courses of gs with a higher accuracy. In particular, it was able to describe the hysteresis of gs responses to increasing/decreasing irradiance and the resulting rapid variations of intrinsic water-use efficiency. Compared to the mechanistic model of temporal responses of gs by Kirschbaum, Gross & Pearcy, for which time constants were estimated with a large variance, our model estimated time constants with a higher precision. It is expected to improve predictions of water loss and water-use efficiency in higher scale models by using a small number of parameters.