Emerging Multi-year Predictability of Heatwave-Driven Cooling Demand

Heatwaves are driving rapidly increasing cooling demand, placing power systems under growing stress with implications for energy resilience in a changing climate. While recent advances in climate prediction have improved understanding of near-term climate variability, the seasonal-to-multiyear predictability of extreme cooling demand remains insufficiently explored. Here, we assess the prediction skill of heatwaves, cooling degree days (CDDs), and derived categories of cooling demand across Northern Hemisphere hotspots using initialized and uninitialized simulations from the Community Earth System Model version 2 Multiyear Prediction System (CESM2-MP) over the period 1981–2020. We evaluate predictability associated with externally forced signals and internal climate variability, with a focus on summer thermal extremes relevant to cooling demand. We find that externally forced signals provide robust seasonal-to-multiyear predictability of terrestrial heatwaves, enabling skillful forecasts of dry and humid CDDs and associated categories of elevated cooling demand at multi-year lead times. Predictive skill is strongest in regions including the US Southwest, Arabian Peninsula, Central America, and Southeast Asia, where forecasts reliably distinguish between below-normal, elevated, and extreme demand years. Internal climate variability, including ENSO-related signals, contributes additional but more limited predictability at approximately one-year lead time. Our results indicate emerging multi-year predictability of heatwave-driven cooling demand, highlighting the potential for climate-informed approaches to anticipate future demand extremes and support energy-system resilience and adaptation planning.

Key words: terrestrial heatwaves, cooling degree days, cooling demand, predictability, external forcing, CESM2-MP, climate extremes, climate risk, energy resilience, hotspots, urban applications, socio-economic and health impacts.