Balancing accuracy versus precision: Enhancing the usability of sub-seasonal forecasts

Forecasts are essential for climate adaptation and preparedness, such as in early warning systems and impact models. A key limitation to their practical use is often their coarse spatial grid spacing. However, another less frequently discussed but crucial limitation is that forecasts are often more precise than they are accurate when their grid spacing is finer than the scales they can accurately predict. Here, we adapt the fractions skill score, a metric conventionally used to quantify spatial forecast accuracy by the meteorological community, to help users navigate the trade-off between forecast accuracy versus precision. We demonstrate how this trade-off can be visualized for daily European precipitation, focusing on deterministic predictions of anomalies and probabilistic predictions of extremes, derived from three years of sub-seasonal forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF). Our results show that decreasing precision through spatial aggregation increases forecast accuracy, extends predictable lead times, and enhances the maximum possible accuracy relative to the grid scale, while increased precision diminishes these benefits. Notably, spatial aggregation benefits daily-accumulated forecasts more than weekly-accumulated ones, per unit lead-time. We demonstrate the practical value of our approach in three examples: communicating early warnings, managing hydropower capacity, and commercial aviation planning—each characterized by distinct user constraints on accuracy, spatial scale, or lead-time. The results suggest a different approach for using forecasts; post-processing forecasts to focus on the most accurate scales rather than the default grid scale, thus offering users more actionable information. • Forecasts often have finer resolution than the scales they can accurately predict. • Developed user-method to evaluate trade-off between forecast accuracy and precision. • Reducing forecast precision improves accuracy and extends predictable lead-times. • Post-processing forecasts to accurate scales yields more actionable information.