  Projections of changes in extreme climate are sometimes predicted by using
multi-model ensemble methods such as Bayesian model averaging (BMA) embedded
with the generalized extreme value (GEV) distribution. BMA is a popular method
for combining the forecasts of individual simulation models by weighted
averaging and characterizing the uncertainty induced by simulating the model
structure. This method is referred to as the GEV-embedded BMA. It is, however,
based on a point-wise analysis of extreme events, which means it overlooks the
spatial dependency between nearby grid cells. Instead of a point-wise model, a
spatial extreme model such as the max-stable process (MSP) is often employed to
improve precision by considering spatial dependency. We propose an approach
that integrates the MSP into BMA, which is referred to as the MSP-BMA herein.
The superiority of the proposed method over the GEV-embedded BMA is
demonstrated by using extreme rainfall intensity data on the Korean peninsula
from Coupled Model Intercomparison Project Phase 5 (CMIP5) multi-models. The
reanalysis data called APHRODITE (Asian Precipitation Highly-Resolved
Observational Data Integration Towards Evaluation, v1101) and 17 CMIP5 models
are examined for 10 grid boxes in Korea. In this example, the MSP-BMA achieves
a variance reduction over the GEV-embedded BMA. The bias inflation by MSP-BMA
over the GEV-embedded BMA is also discussed. A by-product technical advantage
of the MSP-BMA is that tedious `regridding' is not required before and after
the analysis while it should be done for the GEV-embedded BMA.

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