Rapid Statistical-Physical Adversarial Downscaling Reveals Bangladesh's Rising Rainfall Risk in a Warming Climate

TL;DR

This paper introduces a novel statistical-physical machine learning approach for rainfall downscaling, revealing increased extreme rainfall risk in Bangladesh due to climate change, with quantified uncertainties.

Contribution

It develops an integrated downscaling method combining statistics, physics, and machine learning, providing accurate risk assessment and uncertainty estimates for Bangladesh's future rainfall extremes.

Findings

Extreme rainfall risk in Bangladesh is projected to increase by mid-century.

The daily maximum rainfall at a 100-year return period may rise by about 50 mm.

Uncertainty in projections remains significant across different climate models.

Abstract

In Bangladesh, a nation vulnerable to climate change, accurately quantifying the risk of extreme weather events is crucial for planning effective adaptation and mitigation strategies. Downscaling coarse climate model projections to finer resolutions is key in improving risk and uncertainty assessments. This work develops a new approach to rainfall downscaling by integrating statistics, physics, and machine learning and applies it to assess Bangladesh's extreme rainfall risk. Our method successfully captures the observed spatial pattern and risks associated with extreme rainfall in the present climate. It also produces uncertainty estimates by rapidly downscaling multiple models in a future climate scenario(s). Our analysis reveals that the risk of extreme rainfall is projected to increase throughout Bangladesh mid-century, with the highest risk in the northeast. The daily maximum rainfall at a 100-year return period is expected to rise by approximately 50 mm per day. However, using multiple climate models also indicates considerable uncertainty in the projected risk.