Estimating evapotranspiration (ET) is crucial for understanding changes in the water cycle, yet it remains a challenge due to its dynamic nature. Numerous gridded global ET products exist, but no single product serves as a definitive benchmark. Validation of these large-scale ET products against point data is difficult since ET can vary significantly over space unless atmospheric conditions are stable and land-surface cover is uniform. In this study, six widely used global gridded ET products were first cross-compared and then validated using selected Eddy-covariance towers. The products included: ERA5 (Reanalysis based), GLEAM (Remote sensing based), WGHM (Hydrological model based), Fluxcom (Machine learning based) and KF-ET, WA-ET (Water balance based). The comparison is conducted at a 1˚ spatial and monthly temporal resolution from 2003-2016. Significant differences were observed among the ET datasets, with products performing similarly in some regions but varying considerably in others. Specifically, the Amazon, Southern Africa, Eastern India, and Southern China exhibited large discrepancies, characterized by low correlation (R) and high RMSE values. KF-ET closely resembled Fluxcom in R and RMSE, except in the Amazon and Eastern India, regions where the Fluxcom network is sparse. A dedicated network of Eddy-covariance towers is essential for optimal validation, covering at least a few 1˚ grid cells. To ensure fair comparison, 24 Fluxnet sites were selected from 267 global sites of Fluxnet 2015 dataset based on similarity between the site’s land-use/land-cover (LULC) class and the dominant LULC class within the corresponding 1˚ grid. Further the sites with atleast 30% usable data from 2003-2016 were chosen. Analysis revealed that Fluxcom, ERA5, and KF-ET performed similarly, with Fluxcom outperforming at some sites. However, as Fluxcom is directly derived from Fluxnet sites, its superior performance is expected. WGHM, incorporating various irrigation scenarios, proved reliable among hydrological models. The dominant LULC class percentage within a grid influenced the NSE value between Fluxnet sites and gridded products. The linear relationship between NSE and the dominant LULC class percentage underscores the challenge of comparing site-scale and 1˚ grid-scale data.