SeasonBench-EA: A Multi-Source Benchmark for Seasonal Prediction and Numerical Model Post-Processing in East Asia

Mengxuan Chen, Guowen Li, Zou Ziheng, Fang Wang, Jinxiao Zhang, Runmin Dong, Juepeng Zheng, Haohuan Fu

Keywords: seasonal prediction, numerical weather prediction, ensemble forecast correction, machine learning for climate

TL;DR: SeasonBench-EA, a multi-resolution, multi-source benchmark dataset for seasonal climate prediction over East Asia, supporting both machine learning-based forecasting and ensemble post-processing.

Abstract: Seasonal-scale climate prediction plays a critical role in supporting agricultural planning, disaster prevention, and long-term decision making. In particular, reliable forecasts issued 1-6 months in advance are essential for early warning of flood and drought risks associated with precipitation during the East Asian summer monsoon season. However, while the use of machine learning techniques has advanced rapidly in weather and subseasonal-to-seasonal forecasting, partly driven by the availability of benchmark datasets, their application to seasonal-scale prediction remains limited. Existing seasonal prediction primarily relies on ensemble forecasts from numerical models, which, while physically grounded, are subject to biases and uncertainties at long lead times. Motivated by these challenges, we propose SeasonBench-EA, a benchmark dataset for seasonal prediction in East Asia region. It features multi-resolution, multi-source data with both regional and global coverage, integrating ERA5 reanalysis data and ensemble forecasts from multiple leading forecast centers. Beyond key atmospheric fields, the dataset also includes boundary-related variables, such as ocean state, soil and solar radiation, that are essential for capturing seasonal-scale atmospheric variability. Two tasks are defined and evaluated: 1) machine learning-based seasonal prediction using ERA5 reanalysis, and 2) post-processing of seasonal forecasts from numerical model ensembles. A suite of deterministic and probabilistic metrics is provided for tasks evaluation, along with a hindcast assessment focused on precipitation during the East Asian summer monsoon, aligned with model evaluation protocols used in operations. By offering a unified data and evaluation framework, SeasonBench-EA aims to promote the development and application of data-driven methods for seasonal prediction, a challenging yet highly impactful task with board implications for society and public well-being. Our benchmark is available at https://github.com/SauryChen/SeasonBench-EA.

Dataset URL: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/EPEUGO

Code URL: https://github.com/SauryChen/SeasonBench-EA

Primary Area: AL/ML Datasets & Benchmarks for physics (e.g. climate, health, life sciences, physics, social sciences)

Submission Number: 1273