# Learning and Dynamical Models for Sub-seasonal Climate Forecasting: Comparison and Collaboration

arXiv: https://arxiv.org/abs/2110.05196 · HTML: https://mlsanity.com/paper/2110.05196/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 63 references that are in the map.
- First posted on arXiv 29 Sep 2021.

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

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model](https://mlsanity.com/paper/1706.03458/) (2017)
- [Sub-seasonal forecasting with a large ensemble of deep-learning weather prediction models](https://mlsanity.com/paper/2102.05107/) (2021)
- [Application of Deep Convolutional Neural Networks for Detecting Extreme Weather in Climate Datasets](https://mlsanity.com/paper/1605.01156/) (2016)
- [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, 6 of 6)

- [CirT: Global Subseasonal-to-Seasonal Forecasting with Geometry-inspired Transformer](https://mlsanity.com/paper/2502.19750/) (2025)
- [Beyond Ensemble Averages: Leveraging Climate Model Ensembles for Subseasonal Forecasting](https://mlsanity.com/paper/2211.15856/) (2022)
- [Physics-Informed Teleconnection-Aware Transformer for Global Subseasonal-to-Seasonal Forecasting](https://mlsanity.com/paper/2506.08049/) (2025)
- [Applications of machine learning to predict seasonal precipitation for East Africa](https://mlsanity.com/paper/2409.06238/) (2024)
- [Analyzing the Impact of Climate Change With Major Emphasis on Pollution: A Comparative Study of ML and Statistical Models in Time Series Data](https://mlsanity.com/paper/2405.15835/) (2024)
- [Accurate and Efficient Multivariate Time Series Forecasting via Offline Clustering](https://mlsanity.com/paper/2505.05738/) (2025)

## Abstract (arXiv)

Sub-seasonal climate forecasting (SSF) is the prediction of key climate variables such as temperature and precipitation on the 2-week to 2-month time horizon. Skillful SSF would have substantial societal value in areas such as agricultural productivity, hydrology and water resource management, and emergency planning for extreme events such as droughts and wildfires. Despite its societal importance, SSF has stayed a challenging problem compared to both short-term weather forecasting and long-term seasonal forecasting. Recent studies have shown the potential of machine learning (ML) models to advance SSF. In this paper, for the first time, we perform a fine-grained comparison of a suite of modern ML models with start-of-the-art physics-based dynamical models from the Subseasonal Experiment (SubX) project for SSF in the western contiguous United States. Additionally, we explore mechanisms to enhance the ML models by using forecasts from dynamical models. Empirical results illustrate that, on average, ML models outperform dynamical models while the ML models tend to be conservatives in their forecasts compared to the SubX models. Further, we illustrate that ML models make forecasting errors under extreme weather conditions, e.g., cold waves due to the polar vortex, highlighting the need for separate models for extreme events. Finally, we show that suitably incorporating dynamical model forecasts as inputs to ML models can substantially improve the forecasting performance of the ML models. The SSF dataset constructed for the work, dynamical model predictions, and code for the ML models are released along with the paper for the benefit of the broader machine learning community.