  Numerical climate model simulations run at high spatial and temporal
resolutions generate massive quantities of data. As our computing capabilities
continue to increase, storing all of the data is not sustainable, and thus it
is important to develop methods for representing the full datasets by smaller
compressed versions. We propose a statistical compression and decompression
algorithm based on storing a set of summary statistics as well as a statistical
model describing the conditional distribution of the full dataset given the
summary statistics. The statistical model can be used to generate realizations
representing the full dataset, along with characterizations of the
uncertainties in the generated data. Thus, the methods are capable of both
compression and conditional emulation of the climate models. Considerable
attention is paid to accurately modeling the original dataset--one year of
daily mean temperature data--particularly with regard to the inherent spatial
nonstationarity in global fields, and to determining the statistics to be
stored, so that the variation in the original data can be closely captured,
while allowing for fast decompression and conditional emulation on modest
computers.
