  The hazard of pluvial flooding is largely influenced by the spatial and
temporal dependence characteristics of precipitation. When extreme
precipitation possesses strong spatial dependence, the risk of flooding is
amplified due to catchment factors that cause runoff accumulation such as
topography. Temporal dependence can also increase flood risk as storm water
drainage systems operating at capacity can be overwhelmed by heavy
precipitation occurring over multiple days. While transformed Gaussian
processes are common choices for modeling precipitation, their weak tail
dependence may lead to underestimation of flood risk. Extreme value models such
as the generalized Pareto processes for threshold exceedances and max-stable
models are attractive alternatives, but are difficult to fit when the number of
observation sites is large, and are of little use for modeling the bulk of the
distribution, which may also be of interest to water management planners. While
the atmospheric dynamics governing precipitation are complex and difficult to
fully incorporate into a parsimonious statistical model, non-mechanistic
analogue methods that approximate those dynamics have proven to be promising
approaches to capturing the temporal dependence of precipitation. In this
paper, we present a Bayesian analogue method that leverages large,
synoptic-scale atmospheric patterns to make precipitation forecasts. Changing
spatial dependence across varying intensities is modeled as a mixture of
spatial Student-t processes that can accommodate both strong and weak tail
dependence. The proposed model demonstrates improved performance at capturing
the distribution of extreme precipitation over Community Atmosphere Model (CAM)
5.2 forecasts.
