SEAS5-BCSD: a bias-corrected and downscaled global seasonal forecast reference dataset for 1981–2024

Seasonal forecasts offer valuable information on upcoming conditions for the water, energy, and agricultural sectors. However, applications of raw data from global seasonal forecasts are limited, as they can show substantial biases and temporal drifts. In this study, we present a bias-corrected and downscaled global seasonal forecast reference dataset for precipitation and 2 m temperature for 1981 until 2024, provided at monthly resolution. We achieve this with the Bias Correction and Spatial Disaggregation (BCSD) method, combining ECMWF SEAS5 seasonal forecasts with ERA5 reanalysis data. The resulting post-processed product is a spatially refined and improved dataset for a wide range of seasonal applications in the water, energy and agricultural sectors. Unlike existing products, the dataset provides bias-corrected forecasts for all SEAS5 ensemble members over the full hindcast period (1981–2016) and even beyond (until 2024). The dataset spans all global land areas at 0.25° spatial resolution with a forecast lead time of up to seven months. It comprises 25 ensemble members for the period 1981–2016 and 51 ensemble members for 2017–2024. To assess probabilistic forecast quality, we conduct a comprehensive performance evaluation, using the Brier Skill Score (BSS) and the Continuous Ranked Probability Skill Score (CRPSS). The BCSD-corrected temperature forecasts outperform climatology across nearly all regions and lead times, with highest skill in flat and warm regions. Precipitation skill is highest in the tropics and humid regions. Semi-arid areas show solid skill during the rainy season but reduced performance in dry months. This skillful global seasonal forecast reference dataset can now be explored by the community for subsequent forecast evaluation, drought prediction studies, and water resource management applications. The BCSD-corrected seasonal forecast dataset is publicly available as NetCDF data under a Creative Commons Attribution 4.0 International License (CC BY 4.0) at the World Data Center for Climate (WDCC; https://doi.org/10.26050/WDCC/SEAS5-BCSD, Weber et al., 2026).