A large-scale evaluation of available subseasonal precipitation forecast products over the contiguous United States

Abstract. Accurate precipitation predictions at the subseasonal timescale (beyond a week but within a season) could benefit a range of human activities, but are highly challenging to achieve. Research efforts have been made through multi-agency and international collaborations, resulting in numerous forecast products such as those included in the Subseasonal Consortium (SubC) and the Subseasonal-to-Seasonal (S2S) Prediction Project. However, a unified and comprehensive evaluation of the full suite of hindcast datasets from these efforts remains limited, partly due to inconsistencies in hindcast frequency and data periods across products. In this study, we employ the full suite of nineteen precipitation hindcast datasets from the SubC and S2S projects over the contiguous United States (CONUS). The hindcast datasets are temporally aggregated into weekly values and are assessed against a reference dataset derived from the Parameter-elevation Regressions on Independent Slopes Model (PRISM). Overall and seasonal evaluations are carried out using statistical metrics including percentage bias (PBIAS), anomaly correlation coefficient (ACC), and continuous ranked probability score (CRPS). Furthermore, we adopt a baseline-referenced skillfulness approach that accounts for differences in hindcast initialization, frequency, and data periods for a relatively fair comparison among the employed hindcast datasets. Our results indicate widespread overestimations in winter and spring across most hindcast datasets, while underestimations are more likely to be observed in summer and autumn. Predictive accuracy generally declines over forecast lead time and remains marginal beyond week three. Notable variations in predictive skill are observed across regions, seasons, and lead times, with no single hindcast dataset consistently outperforming others. In summary, this work provides valuable references for both forecast end-users and model developers, and highlights the need for context-specific selection of available subseasonal forecast products for downstream applications.