SeasonBench-EA: A Multi-Source Benchmark for Seasonal Prediction and Numerical Model Post-Processing in East Asia
Mengxuan Chen, Li, Zou Ziheng, Fang Wang, Jinxiao Zhang, Runmin Dong, Juepeng Zheng, Haohuan Fu
Seasonal-scale climate prediction is of great importance for agricultural planning, disaster prevention, and decision-making. In particular, extreme precipitation and droughts during the summer monsoon season in East Asia pose significant risks, highlighting the urgent need for reliable forecasts 1-6 months in advance. However, compared to weather forecasting and S2S prediction, seasonal prediction has received limited attention in the AI community. Current approaches primarily rely on ensemble forecasts from numerical prediction systems, which suffer from systematic biases and considerable uncertainty. Motivated by these challenges, we propose SeasonBench-EA, a seasonal prediction benchmark for the East Asia region, featuring multi-resolution and multi-source data with both regional and global coverage. The dataset includes ERA5 reanalysis and ensemble forecasts from several major meteorological agencies. In addition to key atmospheric variables, it also incorporates boundary layer variables, such as ocean, sea ice, soil and solar radiation, which are closely related to seasonal-scale atmospheric processes. The data are provided at hourly, daily and monthly temporal resolutions. Two core tasks are defined and evaluated: 1) machine learning-based prediction using ERA5 data, and 2) post-processing and correction of ensemble forecasts. The benchmark also includes a variety of deterministic and probabilistic evaluation metrics, as well as a hindcast assessment of precipitation prediction during the East Asian summer monsoon, aligned with operational evaluation settings. Overall, SeasonBench-EA aims to provide a systematic data and evaluation framework for seasonal prediction, promoting the development and application of AI methods in this domain.