Machine learning for heatwave prediction: a global scoping review of environmental predictors and modelling practices

As extreme heat events increase in frequency, intensity, and duration due to climate change, forecasting these events has become vital for early warning systems, public health prepared ness, and climate adaptation strategies, especially in the hottest parts of the world. In recent years, machine learning (ML) has increasingly been applied to environmental and meteoro logical data to improve the prediction of heatwaves and extreme heat events. This scoping review examines global peer-reviewed literature on the application of ML techniques for extreme heat prediction using environmental variables. This includes heatwave prediction, environmental and meteorological predictors used in these models, and the geographical Received: Revised: Accepted: Published: Copyright: ©2026bytheauthors. 1 2 3 4 5 6 7 8 9 distribution of existing research. A total of 23 peer-reviewed studies meeting the inclusion 10 criteria were included in the review, following the PRISMA-ScR guidelines. The findings 11 indicate that artificial neural networks and random forest models are among the most 12 frequently reported high-performing approaches for heatwave prediction. Temperature- 13 related variables, especially maximum temperatures, were consistently identified as the 14 most influential predictors across studies. Furthermore, the evidence base was heavily 15 concentrated in Europe and North America, with comparatively limited representation 16 from low- and middle-income countries, despite these regions often experiencing dispro- 17 portionate impacts of climate change and extreme heat exposure. By synthesising current 18 evidence on ML-based heatwave prediction, associated environmental predictors, and 19 geographical research trends, this review provides insights to support the development of 20 more robust, context-aware, and globally representative heatwave forecasting frameworks.