Historically, land use and land cover change representations within Earth System Models and Multisector Dynamic Models (MSD) had limited, or, in the least, very coarse urban components. More recently, many examples of long-term urban land projections at increasing granularities have become available and are compatible with MSD model frameworks. Even so, these projections are too coarse (e.g., ~0.05° or 1-km) and lack the land-class specificity to inform localized studies of urban morphology required for numerical experiments of exposure and adaptation to climate extremes. The Urban High-resolution Hybrid Regional Downscaling (HYBRD) model was developed to couple regional MSD models with localized urban-scale analysis. The HYBRD model includes two components: 1) a series of statistical models that translates coarser urban land and population inputs into morphology-relevant land demand allocations, and 2) a downscaling model that relies on cellular automata techniques to expand or intensify urban land development. Here, we provide two case studies where the Urban HYBRD model was applied to examine future urban morphologies with respect to urban microclimate and building energy demands (Los Angeles) and with respect to risk aversion from coastal flooding hazards (Baltimore). A common observation across these two studies is that HYBRD exposes the strength of localized controls on urban morphology futures. Whereas coarser urban land projections are agnostic to localized and contextualized constraints, the land-limited nature of Los Angeles and the residential housing market responses to coastal flooding hazards in Baltimore can impose strong influences on urban morphology evolution.