# Adaptive Bias Correction for Improved Subseasonal Forecasting

arXiv: https://arxiv.org/abs/2209.10666 · HTML: https://mlsanity.com/paper/2209.10666/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 96 references that are in the map.
- First posted on arXiv 21 Sep 2022.

## Builds on (most cited first, 7 of 7)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Sub-seasonal forecasting with a large ensemble of deep-learning weather prediction models](https://mlsanity.com/paper/2102.05107/) (2021)
- [Explaining black box decisions by Shapley cohort refinement](https://mlsanity.com/paper/1911.00467/) (2019)
- [Improving Subseasonal Forecasting in the Western U.S. with Machine Learning](https://mlsanity.com/paper/1809.07394/) (2018)
- [Sub-Seasonal Climate Forecasting via Machine Learning: Challenges, Analysis, and Advances](https://mlsanity.com/paper/2006.07972/) (2020)

## Built on by (most cited first, 19 of 19)

- [ClimaX: A foundation model for weather and climate](https://mlsanity.com/paper/2301.10343/) (2023)
- [ChaosBench: A Multi-Channel, Physics-Based Benchmark for Subseasonal-to-Seasonal Climate Prediction](https://mlsanity.com/paper/2402.00712/) (2024)
- [Towards an end-to-end artificial intelligence driven global weather forecasting system](https://mlsanity.com/paper/2312.12462/) (2023)
- [CirT: Global Subseasonal-to-Seasonal Forecasting with Geometry-inspired Transformer](https://mlsanity.com/paper/2502.19750/) (2025)
- [DeepPhysiNet: Bridging Deep Learning and Atmospheric Physics for Accurate and Continuous Weather Modeling](https://mlsanity.com/paper/2401.04125/) (2024)
- [Local Off-Grid Weather Forecasting with Multi-Modal Earth Observation Data](https://mlsanity.com/paper/2410.12938/) (2024)
- [SAGE-Eval: Evaluating LLMs for Systematic Generalizations of Safety Facts](https://mlsanity.com/paper/2505.21828/) (2025)
- [Generative emulation of chaotic dynamics with coherent prior](https://mlsanity.com/paper/2504.14264/) (2025)
- [DiffScale: Continuous Downscaling and Bias Correction of Subseasonal Wind Speed Forecasts using Diffusion Models](https://mlsanity.com/paper/2503.23893/) (2025)
- [Beyond Ensemble Averages: Leveraging Climate Model Ensembles for Subseasonal Forecasting](https://mlsanity.com/paper/2211.15856/) (2022)
- [Deconfounding Multi-Cause Latent Confounders: A Factor-Model Approach to Climate Model Bias Correction](https://mlsanity.com/paper/2408.12063/) (2024)
- [Deep Learning Meets Teleconnections: Improving S2S Predictions for European Winter Weather](https://mlsanity.com/paper/2504.07625/) (2025)
- [Locally Adaptive Conformal Inference for Operator Models](https://mlsanity.com/paper/2507.20975/) (2025)
- [Advancing Subseasonal Forecasting with Machine Learning](https://mlsanity.com/paper/2604.16238/) (2026)
- [Applications of machine learning to predict seasonal precipitation for East Africa](https://mlsanity.com/paper/2409.06238/) (2024)
- [Prediction Beyond the Medium Range with an Atmosphere-Ocean Model that Combines Physics-based Modeling and Machine Learning](https://mlsanity.com/paper/2405.19518/) (2024)
- [Symmetric Composition of Anisotropic Operators for Global Subseasonal-to-Seasonal Climate Forecasting](https://mlsanity.com/paper/2602.15040/) (2026)
- [Dual-Scale Temporal Fusion Reveals Structured Predictability in Subseasonal-to-Seasonal Temperature Prediction](https://mlsanity.com/paper/2605.06911/) (2026)
- [S2S-JEPA: Predicting the Predictable at Subseasonal-to-Seasonal Timescales](https://mlsanity.com/paper/2610.03106/) (2026)

## Abstract (arXiv)

Subseasonal forecasting -- predicting temperature and precipitation 2 to 6 weeks ahead -- is critical for effective water allocation, wildfire management, and drought and flood mitigation. Recent international research efforts have advanced the subseasonal capabilities of operational dynamical models, yet temperature and precipitation prediction skills remain poor, partly due to stubborn errors in representing atmospheric dynamics and physics inside dynamical models. Here, to counter these errors, we introduce an adaptive bias correction (ABC) method that combines state-of-the-art dynamical forecasts with observations using machine learning. We show that, when applied to the leading subseasonal model from the European Centre for Medium-Range Weather Forecasts (ECMWF), ABC improves temperature forecasting skill by 60-90% (over baseline skills of 0.18-0.25) and precipitation forecasting skill by 40-69% (over baseline skills of 0.11-0.15) in the contiguous U.S. We couple these performance improvements with a practical workflow to explain ABC skill gains and identify higher-skill windows of opportunity based on specific climate conditions.