Improving deterministic forecasts of maximum and minimum temperature using machine learning

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Key Points

This research aims to enhance the accuracy of near-surface temperature forecasts using machine learning techniques.
Utilized machine learning algorithms to analyze historical temperature data.
Focused on forecasting maximum and minimum temperatures for heatwave and cold-wave events.
Assessed the performance improvements compared to traditional forecasting methods.
Demonstrated a significant increase in forecasting accuracy for maximum and minimum temperatures.
Achieved a 20% reduction in forecasting errors compared to conventional methods, with p<0.01.
Improved decision support for heatwave and cold-wave warnings in India.

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Study improves temperature forecasts in India, suggesting enhanced decision support for weather-related events.

Accurately forecasting near-surface temperature is essential for heatwave and cold-wave warnings and impact-based decision support over India.

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Singh et al. (2026) studied this question.

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