Rl. Wilby, Statistical downscaling of daily precipitation using daily airflow and seasonal teleconnection indices, CLIMATE RES, 10(3), 1998, pp. 163-178
Monthly or seasonal climate variability is seldom captured adequately by hi
gh-resolution statistical downscaling models. However, such deficiences may
, in fact, be an artefact of the failure of many downscaling models to inco
rporate appropriate low-frequency predictor variables. The present study ex
plores the possibility of using variables that characterise both the high-
and low-frequency variability of daily precipitation at selected sites in t
he British Isles. Accordingly, 3 statistical downscaling models were calibr
ated by regressing daily precipitation data for sites at Durham and Kempsfo
rd, UK, against regional climate predictors for the period 1881-1935. Model
1 employed only 1 predictor, the daily vorticity obtained from daily grid-
point mean-sea-level pressure over the British Isles. Model 2 employed both
daily vorticity and seasonal North Atlantic Oscillation Indices (NAOI) as
predictors. Finally, Model 3 employed daily vorticity and seasonal North At
lantic sea-surface temperature (SST) anomalies as predictors. All 3 models
were validated using daily and monthly precipitation statistics at the same
stations for the period 1936-1990. Although Models 2 and 3 did yield impro
vements in the downscaling of the monthly precipitation diagnostics, the en
hancement was only modest relative to Model 1 (the vorticity-only model). N
onetheless, the preliminary results suggest that there may be some merit in
using North Atlantic SST series as a downscaling predictor variable for da
ily/monthly precipitation in the UK. However, further research is required
to determine whether or not the inclusion of teleconnection indices in down
scaling schemes leads to better representations of low-frequency variabilit
y in both present and future climates when General Circulation Model output
s are employed.