Asymptotic models and inference for extremes of spatio-temporal data

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INLA goes extreme: Bayesian tail regression for the estimation of high spatio-temporal quantiles (2018, background): “Fully Bayesian modeling approaches for spatial and temporal extremes often rely on latent processes embedded into the GP parameters to capture trends and dependence (Cooley et al., 2007; Turkman et al., 2010; Bopp and Shaby, 2017).”
Statistical Modelling of Spatial Extremes (2012, methodology): “Turkman et al. (2010) construct a similar but more complex model for space-time properties of wildfires in Portugal, using a random walk to describe the temporal properties, and smoothing for the spatial dependence; their paper also makes suggestions on spatial max-stable modelling with exceedances.”