The WACMOS-ET project aims to advance the development of land evaporation estimates at global and regional scales. Its main objective is the derivation, validation and inter-comparison of a group of existing evaporation retrieval algorithms driven by a common forcing data set. With the project coming to an end, here we present for the first time the results of our global simulations. Four commonly used process-based evaporation methodologies are evaluated: the Penman-Monteith algorithm from the official MODIS evaporation product (PM-MOD), the Global Land Evaporation: the Amsterdam Methodology (GLEAM), the Surface Energy Balance System (SEBS), and the Priestley and Taylor Jet Propulsion Laboratory model (PT-JPL). The resulting global spatiotemporal variability of evaporation, the closure of regional water budgets and the discrete estimation of land evaporation components or sources (i.e. transpiration, interception loss and direct soil evaporation) are investigated using river discharge data, independent global evaporation data sets and results from previous studies. The project carried out an assessment of the performance of these four models, first, at local scales using measurements from eddy-covariance towers and lysimeters. Them, an assessment and inter-comparisons of their performances was done at global level. Results indicate that the Priestley and Taylor based products (PT-JPL and GLEAM) perform overall better for most ecosystems and climate regimes. While all three products adequately represent the expected long-term geographical patterns and temporal seasonality, there is a tendency from PM-MOD to underestimate the flux in the tropics and subtropics. Overall, results from GLEAM and PT-JPL appear more robust when compared against surface water balances from 837 globally-distributed catchments, and against evaporation estimates from ERA-Interim and the Model Tree Ensemble (MTE). Nonetheless, all products manifest large discrepancies during conditions of water stress and drought, and deficiencies in the way evaporation is partitioned into its different components. Overall, the observed inter-product variability implies caution in using a single data set for any large-scale application in isolation. The general finding that different models perform better under different conditions highlights the potential for considering biome- or climate-specific composites of models. Yet, the generation of a multi-product ensemble, with weighting based on validation analyses and uncertainty assessments, appears the best way forward in our long-term goal to develop a robust observational benchmark data set of continental evaporation.