Precipitation is the main driver of the hydrological cycle,and plays a key role in the surface energy cycle by influencing the partitioning of the outgoing energy from land between latent and sensible heat flux.We present an approach that incorporates the physical balance constraints of the surface water and energy cycles to perform a comparison of five global precipitation datasets. These include IMERG, GPCP, GPCC, REGEN, and MERRA2. Our approach involves an objective variational data assimilation technique(DAT) that enforces the simultaneous balance of the linked budgets.The DAT implies adjustments to all the individual components of the budgets simultaneously based on their relative uncertainties while minimizing deviation from their initial estimates. We implement the DAT five times, each time with a different precipitation dataset while maintaining the same estimates of the other components of the budgets which we have developed in recent work. Performance conclusions are determined by the ability of precipitation products to achieve closure of the linked budgets using adjustments that are within their prescribed uncertainty bounds. At the spatial level, we show that precipitation is best estimated by GPCC over the high latitudes, by GPCP over the tropics and by REGEN over North Africa and the Middle-East.IMERG and REGEN appear best over Australia and South Asia. Furthermore, our results give insight into the adequacy of prescribed uncertainties of these products and show that MERRA2, while being less competent than the other four products in estimating precipitation, has the best representation of uncertainties in its precipitation estimates. The spatial extent of our results is not only limited to grid cells with in-situ observations and does not extrapolate performance conclusions from observed regions to less observed regions. Therefore, this approach enables a robust evaluation of precipitation estimates and goes some way to addressing the challenge of validation over observation scarce regions.