A Differentiable Framework for Global Circulation Model Precipitation Bias Correction

2026-04-24

AI Summary

This study addresses the challenges of directly applying Global Climate Model (GCM) precipitation outputs to regional climate applications due to their non-Gaussianity, intermittency, and nonlinear representation of extremes. Conventional statistical and black-box machine learning approaches often lack interpretability, generalizability, and fidelity in preserving long-term trends. To overcome these limitations, this work proposes δCLIMBA (dCLIMBA)—the first differentiable modeling framework for GCM precipitation bias correction. dCLIMBA learns a physically informed, spatiotemporally adaptive mapping between CMIP6 historical simulations and Livneh reanalysis data through end-to-end training. The method accurately reproduces precipitation magnitudes and extreme-event quantile structures across multiple U.S. cities, achieves spatial performance comparable to LOCA2, preserves future climate trends, and substantially mitigates edge biases in unseen regions, offering a balanced combination of interpretability, generalization, and physical consistency.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Humans and AI: Human-in-the-loop Machine Learning
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Systematic biases in Global 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 non-linear extremes. However, traditional statistical methods cannot learn from big data and easily address systematic biases in the GCMs, and while machine learning does provide this flexibility, their black-box type functionality hinders us from understanding these biases completely 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 reference reanalysis datasets (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 is well reproduced across diverse U.S. cities, and spatial patterns perform 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.

Problem

• precipitation bias
• Global Circulation Model
• bias correction
• extreme events
• systematic biases

Innovation

• differentiable bias correction
• spatiotemporally adaptive
• extreme precipitation
• trend preservation
• CMIP6