Rapid Climate Model Downscaling to Assess Risk of Extreme Rainfall in Bangladesh in a Warming Climate

TL;DR

This study develops a novel downscaling method combining data, physics, and machine learning to accurately project future extreme rainfall risks in Bangladesh under climate change, highlighting significant increases and uncertainties.

Contribution

It introduces an integrated downscaling approach that improves spatial risk assessment of extreme rainfall in Bangladesh under future climate scenarios.

Findings

Risk of extreme rainfall increases across Bangladesh.

Projected maximum daily rainfall could rise by 50 mm/day by mid-century.

Uncertainties remain due to model and scenario variations.

Abstract

As climate change drives an increase in global extremes, it is critical for Bangladesh, a nation highly vulnerable to these impacts, to assess future risks for effective adaptation and mitigation planning. Downscaling coarse-resolution climate models to finer scales is essential for accurately evaluating the risk of extremes. In this study, we apply our downscaling method, which integrates data, physics, and machine learning, to quantify the risks of extreme precipitation in Bangladesh. The proposed approach successfully captures the observed spatial patterns and risks of extreme rainfall in the current climate while generating risk and uncertainty estimates by efficiently downscaling multiple models under future climate scenarios. Our findings project that the risk of extreme rainfall rises across the country, with the most significant increases in the northeastern hilly and southeastern coastal areas. Projections show that the daily maximum rainfall for a 100-year return period could increase by approximately 50 mm/day by mid-century and around 100 mm/day by the end of the century. However, substantial uncertainties remain due to variations across multiple climate models and scenarios.