A Spatio-Temporal Deep Learning Approach For High-Resolution Gridded Monsoon Prediction

• URL: http://arxiv.org/abs/2601.02445v1
• Date: Mon, 05 Jan 2026 14:02:04 GMT
• Title: A Spatio-Temporal Deep Learning Approach For High-Resolution Gridded Monsoon Prediction
• Authors: Parashjyoti Borah, Sanghamitra Sarkar, Ranjan Phukan,
• Abstract summary: Indian Monsoon Summer (ISM) is a critical climate phenomenon, impacting the agriculture, economy, and water security of over a billion people.<n>Traditional long-range forecasting has predominantly focused on predicting a single, spatially-averaged seasonal value, lacking spatial detail essential for regional-level management.<n>We introduce a novel learning framework that reframes monsoon prediction as a deep-temporal computer vision task.<n>Using 85 years of ERA5 reanalysis data for predictors and IMD rainfall data for targets, we employ a Convolutional Neural Network (CNN)-based architecture to learn the detail mapping from the complex pre-monsoon period
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The Indian Summer Monsoon (ISM) is a critical climate phenomenon, fundamentally impacting the agriculture, economy, and water security of over a billion people. Traditional long-range forecasting, whether statistical or dynamical, has predominantly focused on predicting a single, spatially-averaged seasonal value, lacking the spatial detail essential for regional-level resource management. To address this gap, we introduce a novel deep learning framework that reframes gridded monsoon prediction as a spatio-temporal computer vision task. We treat multi-variable, pre-monsoon atmospheric and oceanic fields as a sequence of multi-channel images, effectively creating a video-like input tensor. Using 85 years of ERA5 reanalysis data for predictors and IMD rainfall data for targets, we employ a Convolutional Neural Network (CNN)-based architecture to learn the complex mapping from the five-month pre-monsoon period (January-May) to a high-resolution gridded rainfall pattern for the subsequent monsoon season. Our framework successfully produces distinct forecasts for each of the four monsoon months (June-September) as well as the total seasonal average, demonstrating its utility for both intra-seasonal and seasonal outlooks.

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