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Extreme streamflow is a key indicator of flood risk, and quantifying thechanges in its distribution under non-stationary climate conditions is key tomitigating the impact of flooding events. We propose a non-stationary processmixture model (NPMM) for annual streamflow maxima over the central US (CUS)which uses downscaled climate model precipitation projections to forecastextremal streamflow. Spatial dependence for the model is specified as a convexcombination of transformed Gaussian and max-stable processes, indexed by aweight parameter which identifies the asymptotic regime of the process. Theweight parameter is modeled as a function of the annual precipitation for eachof the two hydrologic regions within the CUS, introducing spatio-temporalnon-stationarity within the model. The NPMM is flexible with desirable taildependence properties, but yields an intractable likelihood. To address this,we embed a neural network within a density regression model which is used tolearn a synthetic likelihood function using simulations from the NPMM withdifferent parameter settings. Our model is fitted using observational data for1972--2021, and inference carried out in a Bayesian framework. The two regionswithin the CUS are estimated to be in different asymptotic regimes based on theposterior distribution of the weight parameter. Annual streamflow maximaestimates based on global climate models for two representative climate pathwayscenarios suggest an overall increase in the frequency and magnitude of extremestreamflow for 2006-2035 compared to the historical period of 1972-2005.

A Deep Learning Synthetic Likelihood Approximation of a Non-stationary Spatial Model for Extreme Streamflow Forecasting