Catchment-scale skill assessment of seasonal precipitation forecasts across South Korea

Climate change is expected to make droughts more frequent and severe all over the world. South Korea is no exception and is already suffering from extreme droughts such as one that prolonged from 2013 to 2015 and caused nation-wide damage. To mitigate drought damages, better management of existing water infrastructure is essential. A promising opportunity to improve operational decisions is to make use of seasonal weather forecasts that are provided by general circulation models and can be downscaled to the catchment scale. This study hence assesses the skill of seasonal forecasts over 20 catchments in South Korea, where the largest reservoirs are located. Datasets from four weather forecasting centres (ECMWF, UK Met Office, Météo France and DWD) have been evaluated over the period 2011–2020, and their skill quantified using the Continuous Ranked Probability Skill Score (CRPSS). We analyse how skill varies across the seasons and years, and if it can be linked to catchments characteristics. In doing so, we develop a methodology and a Python package to implement it, which is freely available for future applications to other regions. As for the study case, our results showed that among the four forecasting centres, ECMWF's forecasts were the most skilful in South Korea. In particular, seasonal forecasts outperform the climatology for 2 months of lead time and are more skilful during the wet season and in dry years. Linear bias correction is found to be useful to correct systematic seasonal biases, whereas we found no significant correlation between the catchment characteristics and forecast skill. We also investigated the possibility of anticipating dry years from seasonal forecasts and/or ENSO indices but found no significant link.