Hot, dry start to 2026 lifts Australian PV, despite fires and monsoon losses

Solcast, a DNV company, reports that most of Australia experienced above-average solar irradiance in January 2026 due to a hot, dry airmass and clear skies, boosting PV generation despite operational challenges from elevated temperatures, bushfire smoke, and dust. In contrast, northern Queensland and far northern regions saw significant irradiance deficits.

February 17, 2026 Solcast

Image: Solcast

Most of Australia recorded above average irradiance in January 2026 as a hot, dry continental airmass dominated the month, despite localised reductions from bushfire smoke, dust, and an active monsoon in the tropical north, according to analysis using the Solcast API. While widespread sunshine supported strong conditions for PV generation, elevated temperatures, smoke events, and dust soiling created operational challenges. In contrast, northern Queensland and far north of Australia experienced significant irradiance deficits under persistent monsoonal cloud and Tropical Cyclone Koji.

Dry conditions prevailed across the majority of the continent, lifting monthly average irradiance 10% to 15% above the January climatology, particularly across inland New South Wales (NSW), Queensland, South Australia, and Western Australia. These clear-sky conditions were associated with a slow-moving upper-level pattern that supported the development of a persistent heat dome over the continent. This feature suppressed cloud formation and rainfall, while periodically directing hot northerly winds into southeastern Australia ahead of cold fronts. Nationally, monthly average temperatures were 2.3 C above normal, exceeding 3 C above average in parts of interior NSW and southwest Queensland. While clear skies favoured irradiance, module operating temperatures would have been elevated, reducing panel efficiency during peak heat periods. The scale of the above-average warmth is consistent with broader trends observed in a warming climate.

The hot and dry pattern also heightened bushfire risk and dust mobilisation, creating additional complexity for PV operations. Strong northerly winds preceding southern cold fronts drove dangerous fire weather in Victoria and NSW, with smoke temporarily reducing irradiance at nearby sites. In Bendigo, analysis of particulate matter concentrations and rainfall indicates a noticeable uptick in estimated soiling losses around 11 January, coinciding with nearby fire activity. Subsequent rainfall helped wash panels clean, limiting the persistence of these losses. Although fire episodes reduced irradiance locally and temporarily, they did not materially alter the overall monthly above-average signal, as similar events appear in the historical January baseline.

Further inland, prolonged dryness enabled dust uplift. In Alice Springs, elevated PM10 concentrations on 12 and 13 January were associated with a marked rise in estimated soiling losses. With minimal rainfall to offset accumulation, losses increased from 0.024 to 0.032 over the month, equivalent to around 0.8% additional reduction due to dust deposition. A large dust storm late in the month, moving into western NSW, underscored the vulnerability of central Australian sites during extended dry spells.

In contrast to the national trend, tropical regions saw substantial irradiance deficits. Cape York Peninsula recorded values around 20% below average, while the far north of Australia and parts of northeast Queensland near Townsville and Mackay were approximately 10% below normal. A persistent monsoon trough brought widespread cloud and rainfall across northern Australia. Tropical Cyclone Koji, named on 10 January in the Coral Sea, further reduced irradiance as thick cloud covered coastal Queensland between Townsville and Mackay. As the system weakened inland, heavy rainfall and flooding followed, with more than 22,000 homes experiencing power outages due to wind and rain damage.

Solcast produces these figures by tracking clouds and aerosols at 1-2km resolution globally, using satellite data and proprietary AI/ML algorithms. This data is used to drive irradiance models, enabling Solcast to calculate irradiance at high resolution, with typical bias of less than 2%, and also cloud-tracking forecasts. This data is used by more than 350 companies managing over 300 GW of solar assets globally.

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