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

TL;DR

This paper presents a novel deep learning framework that models monsoon prediction as a spatio-temporal computer vision task, enabling high-resolution, region-specific forecasts from historical climate data.

Contribution

It introduces a CNN-based approach that predicts detailed gridded monsoon rainfall patterns using multi-variable atmospheric and oceanic data as video-like inputs.

Findings

Successfully predicts month-by-month rainfall patterns

Produces high-resolution gridded forecasts for the entire monsoon season

Demonstrates utility for intra-seasonal and seasonal climate outlooks

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.