Harnessing Subseasonal‐to‐Seasonal Predictability for Wind Energy in Kenya

ABSTRACT Understanding how climate variability influences wind‐power resources is critical for energy security, particularly in countries such as Kenya where reliance on weather‐sensitive generation is increasing. Using ERA5 reanalysis, climate‐mode indices, site observations where available and modelled wind‐power potential, we quantify how three major tropical climate modes relevant to subseasonal‐to‐seasonal (S2S) wind‐power variability, the Madden–Julian Oscillation (MJO), El Niño–Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD), influence Kenya's wind‐energy system. Results show that MJO Phases 2–4 generally suppress wind‐energy potential across Kenya, while Phases 6–8 enhance it. ENSO influences are weaker and more seasonally selective, with El Niño favouring suppressed wind‐energy potential during October–November–December and La Niña favouring enhancement during April–May–June. The IOD imprint is strongest in October–November–December, when positive IOD conditions weaken wind‐energy potential across key inland regions and negative IOD conditions strengthen it. Using a perfect‐forecast framework that assumes known MJO, ENSO and IOD states, with forecast skill evaluated through leave‐one‐out cross‐validation, we quantify the potential value of climate‐mode information for wind‐power prediction. We show that conditioning on the MJO provides the largest single‐mode improvements in deterministic and probabilistic wind‐power forecast skill relative to monthly climatology. Conditioning on ENSO and the IOD yields smaller, season‐dependent skill gains, with the strongest improvements occurring in October–November–December. Combined MJO–ENSO–IOD conditioning gives the largest overall skill improvement. Taken together, these results identify a clear hierarchy of predictability: MJO information provides the strongest subseasonal contribution to wind‐power prediction, while ENSO and IOD provide broader seasonal context and smaller refinements. This hierarchy supports the development of climate‐mode‐informed “windows of risk and opportunity” that translate S2S climate information into practical services for anticipatory, risk‐informed wind‐energy operations and planning in Kenya, with potential relevance for wider East Africa.