Interannual variability of Great Plains precipitation in the warm-season months is analyzed using gridded observations, satellite-based precipitation estimates, NCEP and ERA-40 reanalysis data, and the half-century long NCAR/CAM and NASA/NSIPP AMIP simulations. Regional hydroclimate is the focus because of its immense societal impact, and because the involved variability mechanisms are not well understood. The latter is amply reflected in the simulation potential of the analyzed models, and in the extent to which hydroclimate variability is captured in the two reanalyses. We find that Great Plains precipitation variability is represented rather differently, and only quasi-realistically, in the reanalyses. NCEP has larger amplitude but less traction with observations in comparison with ERA-40. Model simulations exhibit even greater amplitude-spread, with NSIPP’s being larger than NCEP’s, and CAM’s being smaller than ERA-40. The simulated variability is moreover uncorrelated with observations, in both models; monthly correlations are smaller than 0.10 in all cases. Closer examination reveals that models generate nearly all of their Great Plains precipitation from deep convective processes, whereas ERA-40 suggests that stratiform contribution is, at least, as important as the convective one. An assessment of the regional atmosphere water-balance is also revealing: Stationary moisture-flux convergence accounts for most of the Great Plains variability in ERA-40, but not in the NCEP reanalysis and model simulations; convergent fluxes generate less than half of the precipitation in the latter; local evaporation does the rest in models. Phenomenal evaporation in the models – up to four-times larger than the highest observationally constrained estimate (NCEP’s) – provides bulk of the moisture for Great Plains precipitation variability, i.e., precipitation-recycling is very efficient in models; perhaps, too efficient, specially in the NSIPP model. Remote water sources contribute substantially to Great Plains hydroclimate variability in nature, via fluxes. Getting the interaction pathways right is presently challenging for models.