Hydroclimate extreme events, especially precipitation and streamflow extremes during wet seasons, pose severe threats to life, livelihoods, and infrastructure. Therefore, timely and skillful projections of attributes of seasonal streamflow extremes are imperative to plan mitigation strategies. In particular, the number of ‘events’ – i.e., exceedances of flow thresholds that result in flooding and the magnitude of such extremes during the season, will be of immense use to policymakers for early planning and implementation of flood risk mitigation and adaptation strategies. However, predicting seasonal extremes is challenging, particularly under spatial and temporal non-stationarity. To address this need, we develop a space-time model to project seasonal flow risk attributes using a Bayesian hierarchical modeling (BHM) framework in this study. In this model, the number of events exceeding a threshold during a season at a suite of gauge locations on a river network are modeled as Poisson margins. The seasonal daily maximum flows are modeled as a generalized extreme value (GEV). The rate parameters of the Poisson distribution and scale and shape parameters of the GEV are modeled as a linear function of suitable covariates. Gaussian Elliptical Copulas are applied to capture the spatial dependence. The best set of covariates is selected using the leave-one-out cross-validation information criteria (LOOIC). The modeling framework results in the posterior distribution of the risk attributes for each season and, thus, the uncertainties. We demonstrate the utility of this modeling framework to project the flood risk attributes during the summer peak monsoon season (July-August) at five gauges in the Narmada River basin of West-Central India. As potential covariates, we consider climate indices such as El Niño–Southern Oscillation (ENSO), the Indian Ocean Dipole (IOD), and the Pacific Warm Pool Region (PWPR) from the precedent season, which have shown strong teleconnections with the Indian monsoon. This spatiotemporal modeling framework helps in the planning of seasonal adaptation and preparedness measures as predictions of monsoon high flow risk occurrence become available up to 3 months before actual flood occurrence.