Variability of hydroclimate (precipitation, streamflow, temperature etc.) and extremes in space-time pose major natural hazard risk to human life and infrastructure. This risk is exacerbated in developing communities with marginal infrastructure and response capabilities. Thus, robust understanding, modeling and projection of risk is crucial to devise effective hazard mitigation strategies at operational and planning time scales. This requires methods that capture the uncertainties in the hydroclimate process robustly. To this end, Bayesian hierarchical modeling (BHM) methods for modeling hydroclimate variables such as, precipitation, streamflow, extremes, etc. offer attractive alternatives to traditional frequentist counterparts. Their attraction is their ability to capture uncertainties robustly besides, enabling to incorporate qualitative and quantitative information about the underlying processes. In recent past, a suite of BHMs for various hydroclimate applications from several research groups including ours have emerged. This spans - stochastic space-time weather generation, precipitation extremes in space and time, streamflow and extremes on river network, Arctic sea-ice retreat and paleoclimate reconstruction. In this talk, I will provide an overview of the BHM models for hydroclimate risk assessment. Then, will focus on two novel application models – (i) daily flow and extremes on a river network on the Narmada River Basin and Upper Colorado Basin in USA and (ii) space-time precipitation field. These models provide posterior distributions, consequently, ensembles, of streamflow and precipitation extremes. Which enables to obtain estimates of return levels, important for risk assessment and management.