A collection of eight operational global analyses over a 27 month period have been processed to common data structures to facilitate comparisons among the analyses and global observational data sets. In the present study, we evaluate the global precipitation, outgoing longwave radiation (OLR) at the top of the atmosphere, and basin-scale precipitation over the United States. In addition, a multi-model ensemble is created from a linear average of the available data, as close to the analysis time as each system permits. The results show that the monthly global precipitation and OLR from the multi-model ensemble compares generally better to the observations than any single analysis. Likewise, the daily precipitation from the ensemble exhibits better statistical comparison (in space and time) to gauge observations over the Mississippi River Basin. However, the comparisons have seasonality, where the members of the ensemble exhibit generally more skill, during winter. There is notably higher skill of the summer time basin precipitation by the ensemble. Using the global precipitation and OLR, the sensitivity is tested to selectively choosing the members with the best statistical comparisons to the reference data. Only small improvements in the statistics are found when comparing a selective ensemble to the full ensemble. Additionally, terms of the global energy budget are compared among the ensemble and to other estimates. The ensemble data and the variance of the ensemble should make a useful point of comparison for the development of model and assimilation components of global analyses.