Attribution of human influences on coastal flooding has now been demonstrated, with a recent study showing that Hurricane Sandy's flooding and damages were made worse by anthropogenic sea level rise. Other recent efforts have sought to eliminate delays between weather events and climate attribution, performing rapid attribution or forecast-hindcast analyses to quickly attribute events and capitalize on immediate media interest. In this presentation we will combine these approaches, demonstrating a coastal flood attribution framework using a one-year model simulation of flooding for Jamaica Bay, New York City. Preliminary results show that of 17 minor flood exceedances in 2020, only 7 would have occurred without anthropogenic sea level rise. Only 2 would have occurred without historical landscape change, predominantly dredging and landfill used to convert a lagoonal system into a deepwater port. We also demonstrate a real-time climate attribution case study for the Jamaica Bay domain using water level forecasts from the Stevens Flood Advisory System, a regional ensemble total water level forecast system, as boundary conditions on our domain. Forecasts will be created prior to a flood, and hindcasts the day afterward to provide final estimates of flooding (and damages, based on the FEMA HAZUS model). Throughout 2020 tropical cyclones provided the potential for flooding due to intense rainfall across the region, and the attribution of these compound flooding events is also explored. The goal of this research is to demonstrate and operationalize a new approach for climate attribution that could be applied nationwide, serving up real-time data on the impacts of climate change to residents and the media.