Reinforcement Learning to Discover a NorthEast Monsoon Index for Monthly Rainfall Prediction in Thailand

• URL: http://arxiv.org/abs/2601.10181v2
• Date: Fri, 16 Jan 2026 01:49:39 GMT
• Title: Reinforcement Learning to Discover a NorthEast Monsoon Index for Monthly Rainfall Prediction in Thailand
• Authors: Kiattikun Chobtham,
• Abstract summary: This paper introduces a novel NorthEast monsoon climate index calculated from sea surface temperature to reflect the climatology of winter monsoon.<n>To optimise the calculated areas, a Deep Q-Network learning agent explores and selects the most effective rectangles based on their correlation with seasonal rainfall.<n> Experimental results show that the optimised index into Short-Term Memory models significantly improves long-term monthly rainfall prediction skill in most cluster areas.
• Score: 0.456877715768796
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Climate prediction is a challenge due to the intricate spatiotemporal patterns within Earth systems. Global climate indices, such as the El Niño Southern Oscillation, are standard input features for long-term rainfall prediction. However, a significant gap persists regarding local-scale indices capable of improving predictive accuracy in specific regions of Thailand. This paper introduces a novel NorthEast monsoon climate index calculated from sea surface temperature to reflect the climatology of the boreal winter monsoon. To optimise the calculated areas used for this index, a Deep Q-Network reinforcement learning agent explores and selects the most effective rectangles based on their correlation with seasonal rainfall. Rainfall stations were classified into 12 distinct clusters to distinguish rainfall patterns between southern and upper Thailand. Experimental results show that incorporating the optimised index into Long Short-Term Memory models significantly improves long-term monthly rainfall prediction skill in most cluster areas. This approach effectively reduces the Root Mean Square Error for 12-month-ahead forecasts.

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