Abstract - Although there is a large body of evidence from observations and model studies linking precipitation occurrence to land-surface conditions, the space-time dynamics by which land-atmosphere feedback mechanisms are reflected in the dynamics of moist processes in the mid and upper troposphere are not well understood. The challenge is to isolate the contribution of land controls to the predictability of clouds and precipitation. Furthermore, metrics are necessary that can support diagnostic studies and also permit systematically exploring the range of models' behavior that is consistent with observations. We present a framework to characterize the pathways of dynamical coupling by which land-atmosphere interactions influence, or modulate the predictability of hydrometeorological phenomena regionally. For this purpose, we conduct diagnostic data analysis of NCEP Regional Reanalysis and ISCCP cloud data in North America. Concrete metrics consist of spatial fields of nonlinear dynamics measures such as Finite-Size Lyapunov exponents and space-time coupling indices including directionality, synchronicity and diffusion to assess predictability.