Downscaling with Constructed Analogues: Daily Precipitation and Temperature Fields Over the United States

Daily precipitation and average temperature patterns for the contiguous United States were
downscaled from a 2.5 x 2.5 degree (coarse) resolution grid to a 1/8 x 1/8 degree (fine) resolution
grid using a constructed‐analogues method. Choice of predictors, and the selection of subsets of
most‐suitable historical dates to be included in the constructed analogues proved to be
important determinants of the method’s skill, especially for precipitation. The downscaling
method skillfully reproduces daily variations of precipitation and average temperature
anomalies, as well as seasonal cycles, across the contiguous United States. The method tends to
overestimate the number of wet days, producing a very light “drizzle” on many of the
effectively dry days. There are also biases in the monthly climatologies of precipitation and
average temperature in some regions, which tend to average out at annual timescales.
Averaging daily downscaled patterns into monthly means yielded even more skillful results,
capturing about 55 percent of the variations of monthly precipitation anomalies and about 80
percent of the variations of average temperature monthly anomalies across the contiguous
United States. The choice of the domain of the predictor also influences the skill. For example, in
California, the most skillful precipitation downscaling was obtained when the precipitation
predictors covered the state, whereas average temperature downscaling was most skillful when
average temperature predictors included continent‐wide patterns. Overall, the method showed
encouraging results for downscaling daily precipitation and average temperature continentalwide
patterns in North America—in particular, those of the western United States.