A Spatio-Temporal Deep Learning Approach For High-Resolution Gridded Monsoon Prediction
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arXiv
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| Formato: | Preprint |
| Publicado: |
2026
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| author | Borah, Parashjyoti Sarkar, Sanghamitra Phukan, Ranjan |
| author_facet | Borah, Parashjyoti Sarkar, Sanghamitra Phukan, Ranjan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_02445 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Spatio-Temporal Deep Learning Approach For High-Resolution Gridded Monsoon Prediction Borah, Parashjyoti Sarkar, Sanghamitra Phukan, Ranjan Computer Vision and Pattern Recognition Machine Learning I.2.6; I.4.9; I.5.1; I.5.2; I.5.4; I.5.5; I.5.m; I.6.5 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. |
| title | A Spatio-Temporal Deep Learning Approach For High-Resolution Gridded Monsoon Prediction |
| topic | Computer Vision and Pattern Recognition Machine Learning I.2.6; I.4.9; I.5.1; I.5.2; I.5.4; I.5.5; I.5.m; I.6.5 |
| url | https://arxiv.org/abs/2601.02445 |