It is a major challenge to develop gridded precipitation and temperature estimates that adequately
resolve the extreme spatial gradients present in the Hawaiian Islands. The challenge is
particularly pronounced because the available station networks are irregularly spaced and sparse,
creating large uncertainties in gridded spatial meteorological estimates. Here we develop a 100-
member, daily ensemble of precipitation and temperature estimates over the Hawaiian Islands for
the period 1990-2014 at 1 km grid resolution. We first develop an intermediary ensemble
estimate of the monthly climatological precipitation and temperature and use the climatological
surfaces to inform daily anomaly interpolation. This Climatologically Aided Interpolation (CAI)
method extends our initial ensemble system developed for the continental United States
(CONUS).
For Hawaii, we show that daily interpolation using only daily data is inferior to the CAI
methodology, particularly over longer time periods (years to decades). Daily interpolation has
more value for short time periods (e.g. 1-month or less) or when the precipitation distribution
substantially diverges from climatology. The CAI ensemble is able to reproduce observed
precipitation and temperature patterns, including the probability of precipitation. Leave-one-out
cross-validation results illustrate that the ensemble has: 1) minimal biases, 2) reasonable mean
absolute errors, 3) good representation of daily precipitation intensity variability, and 4) good
reliability and discrimination. Additionally, the ensemble is able to reasonably reproduce the
tails (e.g. 99.9th percentile) of the daily precipitation and temperature distributions, with
increasing uncertainty for higher percentiles.
