One difficulty that arises when predicting canopy-scale energy fluxes is that the parameterization of complex (bio)physical soil vegetation atmosphere transfer schemes is often only partially conditioned by the information content of the eddy covariance data commonly used for calibration, rendering subsequent predictions and extrapolations somewhat uncertain. Here we derive a functional description for daily evaporative fluxes directly from observations at the canopy scale using a nonstationary regression framework. This method is applied to 3 year blocks of eddy covariance and micrometeorological data from three different FLUXNET forest sites: Harvard Forest, USA, University of Michigan Biological Station, USA, and Hyyti¿l¿, Finland, covering a variety of climate and vegetation conditions. The approach yields a simple three-parameter model which is based on partitioning latent heat between equilibrium latent heat fluxes and fluxes that are under strong stomatal control and hence are related to CO2 fluxes. Despite being well defined and able to account for much of the observed variations in latent heat, predictive validation of the model emphasizes the need to account for surface and subsurface water balance in such descriptions.