Prediction of seasonal precipitation can provide actionable information to
guide management of various sectoral activities. For instance, it is often
translated into hydrological forecasts for better water resources management.
However, many studies assume homogeneity in precipitation across an entire
study region, which may prove ineffective for operational and local-level
decisions, particularly for locations with high spatial variability. This
study proposes advancing local-level seasonal precipitation predictions by
first conditioning on regional-level predictions, as defined through
objective cluster analysis, for western Ethiopia. To our knowledge, this is
the first study predicting seasonal precipitation at high resolution in this
region, where lives and livelihoods are vulnerable to precipitation
variability given the high reliance on rain-fed agriculture and limited water
resources infrastructure. The combination of objective cluster analysis,
spatially high-resolution prediction of seasonal precipitation, and a
modeling structure spanning statistical and dynamical approaches makes clear
advances in prediction skill and resolution, as compared with previous
studies. The statistical model improves versus the non-clustered case or
dynamical models for a number of specific clusters in northwestern Ethiopia,
with clusters having regional average correlation and ranked probability skill score (RPSS) values of up to
0.5 and 33β―%, respectively. The general skill (after bias correction) of
the two best-performing dynamical models over the entire study region is
superior to that of the statistical models, although the dynamical models
issue predictions at a lower resolution and the raw predictions require bias
correction to guarantee comparable skills
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