A Differentiable Framework for Global Circulation Model Precipitation Bias Correction

• URL: http://arxiv.org/abs/2604.23045v2
• Date: Thu, 30 Apr 2026 21:36:13 GMT
• Title: A Differentiable Framework for Global Circulation Model Precipitation Bias Correction
• Abstract summary: CLIMBA learns a parametric bias adjustment procedure between historical General Model (GCM) outputs and a gridded observation-based dataset (Livneh Circulation)<n>Results demonstrate that the proposed method accurately corrects both the magnitude and distribution of extreme storm events, with particularly strong performance in capturing extremes.<n>This work presents a modular, computationally efficient, and bias correction approach that is physically informed, scalable, and compatible with both historical and future applications.
• Score: 2.0225826789157404
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Systematic biases in General Circulation Model (GCM) outputs limit their direct applicability in regional planning, necessitating bias correction. Correcting precipitation is particularly challenging due to its non-Gaussian distribution, intermittent nature, and heavy-tailed extremes. However, traditional statistical methods cannot learn from big data or easily address systematic biases in GCMs. While machine learning does provide this flexibility, its black-box type functionality hinders us from understanding these biases which also further prevents generalization across different GCMs and locations, especially for precipitation. In this study, we propose a differentiable bias adjustment framework called δCLIMBA (or dCLIMBA), that learns a spatiotemporally-adaptive parametric bias adjustment procedure between historical CMIP6 model outputs and a gridded observation-based dataset (Livneh). Results demonstrate that the proposed method accurately corrects both the magnitude and distribution of extreme storm events, with particularly strong performance in capturing extremes. The quantile distribution of precipitation was well reproduced across diverse U.S. cities, and spatial patterns performed comparably to the widely used LOCA2 statistical downscaling technique. In addition, the framework showed future trend preservation unlike pure quantile based methods and LOCA2, and results from bias correction over unseen regions showed that the marginal biases were attenuated. This work presents a modular, computationally efficient, and extensible bias correction approach that is physically informed, scalable, and compatible with both historical and future applications. Its flexibility makes it suitable for integration into Earth system post-processing pipelines and impact workflows.

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