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Europe's solar resource remained above average through August, before a weakening weather pattern brought shifting irradiance conditions later in the month.

July 2026 was the hottest month ever recorded in the United States, yet North American irradiance remained close to long-term averages. Wildfire smoke and localized storms offset what could have been a stronger solar resource month.

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Eclipse timing played an important role in shaping daily solar losses during the 12 August event, while cloud conditions further influenced observed impacts. The study also examined electricity-market responses across Spain, France and New York.

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Solcast forecasts suggest eclipse timing could influence solar and grid impacts as much as eclipse depth. Explore the projected impacts across Europe and North America.

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Using four leading seasonal weather models and first-half 2026 observations, Solcast analyses how a strengthening El Niño is expected to reshape global solar irradiance in late 2026, showing regional outlooks and forecast confidence.

Europe experienced widespread irradiance gains, while El Niño, cloud, rainfall, dust and cyclones reshaped solar resource across North America, Asia, South America and Australia.

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Solcast has improved day-ahead solar forecast accuracy by ~9% in North America using an attention-based ensemble correction layer that targets systematic errors in NWP models. The update improves the reliability of short-term forecasts used for dispatch planning and operational decision-making.

Coastal East Asia experienced one of its sunniest Mays in years, with irradiance gains across Japan, Korea, eastern China, Taiwan and the northern Philippines, while inland China saw heavier late-month rainfall.

The developing El Niño is expected to strengthen through 2026, and analysis of past strong El Niño events in 2009, 2015 and 2023 shows how sharply solar resource shifts between regions during the July–September window.

As solar projects grow more complex, SolarFarmer’s 3D API brings bankable yield modelling into Python workflows, making sensitivity analysis, verification and portfolio-scale automation easier to run and repeat.

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