Subseasonal prediction of April Siberian-Arctic heatwaves using a dynamical-statistical approach

Siberian-Arctic heatwaves (SAHs) disrupt ecosystems by increasing wildfires, thawing permafrost, and threatening Arctic communities. As SAHs become more frequent and intense, accurate prediction is crucial for preparedness and mitigating their impacts. We demonstrate that April surface temperatures in the Siberian Arctic can be predicted one month in advance with a skill of 0.75 (1979–2022) using a regression model based on Arctic stratospheric ozone, the Arctic Oscillation, and sea ice in the Kara Sea. This model successfully predicts six of seven SAHs, identifying three driven by extreme ozone depletion and three by significant sea ice loss. Additionally, from 1979 to 1997, warming was primarily caused by ozone depletion, while from 1998 to 2022, sea ice loss became the main factor. Our findings indicate that SAHs are predictable and recommend this model for real-time monitoring and forecasting, highlighting its potential to enhance preparedness and reduce adverse effects.