  Extreme environmental events frequently exhibit spatial and temporal
dependence. These data are often modeled using max stable processes (MSPs).
MSPs are computationally prohibitive to fit for as few as a dozen observations,
with supposed computationally-efficient approaches like the composite
likelihood remaining computationally burdensome with a few hundred
observations. In this paper, we propose a spatial partitioning approach based
on local modeling of subsets of the spatial domain that delivers
computationally and statistically efficient inference. Marginal and dependence
parameters of the MSP are estimated locally on subsets of observations using
censored pairwise composite likelihood, and combined using a modified
generalized method of moments procedure. The proposed distributed approach is
extended to estimate spatially varying coefficient models to deliver
computationally efficient modeling of spatial variation in marginal parameters.
We demonstrate consistency and asymptotic normality of estimators, and show
empirically that our approach leads to a surprising reduction in bias of
parameter estimates over a full data approach. We illustrate the flexibility
and practicability of our approach through simulations and the analysis of
streamflow data from the U.S. Geological Survey.
