  Quantitative assessment of climate change risk requires a method for
constructing probabilistic time series of changes in physical climate
parameters. Here, we develop two such methods, Surrogate/Model Mixed Ensemble
(SMME) and Monte Carlo Pattern/Residual (MCPR), and apply them to construct
joint probability density functions (PDFs) of temperature and precipitation
change over the 21st century for every county in the United States. Both
methods produce $likely$ (67% probability) temperature and precipitation
projections consistent with the Intergovernmental Panel on Climate Change's
interpretation of an equal-weighted Coupled Model Intercomparison Project 5
(CMIP5) ensemble, but also provide full PDFs that include tail estimates. For
example, both methods indicate that, under representative concentration pathway
(RCP) 8.5, there is a 5% chance that the contiguous United States could warm by
at least 8$^\circ$C. Variance decomposition of SMME and MCPR projections
indicate that background variability dominates uncertainty in the early 21st
century, while forcing-driven changes emerge in the second half of the 21st
century. By separating CMIP5 projections into unforced and forced components
using linear regression, these methods generate estimates of unforced
variability from existing CMIP5 projections without requiring the
computationally expensive use of multiple realizations of a single GCM.
