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Prediction of seasonal precipitation can provide actionable information to\udguide management of various sectoral activities. For instance, it is often\udtranslated into hydrological forecasts for better water resources management.\udHowever, many studies assume homogeneity in precipitation across an entire\udstudy region, which may prove ineffective for operational and local-level\uddecisions, particularly for locations with high spatial variability. This\udstudy proposes advancing local-level seasonal precipitation predictions by\udfirst conditioning on regional-level predictions, as defined through\udobjective cluster analysis, for western Ethiopia. To our knowledge, this is\udthe first study predicting seasonal precipitation at high resolution in this\udregion, where lives and livelihoods are vulnerable to precipitation\udvariability given the high reliance on rain-fed agriculture and limited water\udresources infrastructure. The combination of objective cluster analysis,\udspatially high-resolution prediction of seasonal precipitation, and a\udmodeling structure spanning statistical and dynamical approaches makes clear\udadvances in prediction skill and resolution, as compared with previous\udstudies. The statistical model improves versus the non-clustered case or\uddynamical models for a number of specific clusters in northwestern Ethiopia,\udwith clusters having regional average correlation and ranked probability skill score (RPSS) values of up to\ud0.5 and 33 %, respectively. The general skill (after bias correction) of\udthe two best-performing dynamical models over the entire study region is\udsuperior to that of the statistical models, although the dynamical models\udissue predictions at a lower resolution and the raw predictions require bias\udcorrection to guarantee comparable skills

Does objective cluster analysis serve as a useful precursor to seasonal precipitation prediction at local scale? Application to western Ethiopia