Relative skills of SPEAR and CFSv2 in foretelling warm-season precipitation anomalies over the conterminous United States and potential sources of errors

This study evaluates the skill of NOAA’s Seamless System for Prediction and Earth System Research (SPEAR) and the NCEP Climate Forecast System version 2 (CFSv2) in forecasting spring and summer precipitation anomalies over the contiguous United States (CONUS) during 1991–2022. Using anomaly correlation coefficient, percentage bias, bootstrap significance testing, and quantile-based categorical metrics, we assess seasonal-mean precipitation and wet extremes of seasonal-mean precipitation, defined as exceedances of the 80th and 90th percentile thresholds. Coupled singular value decomposition (SVD) is further used to diagnose tropical Pacific sea surface temperature (SST)–CONUS precipitation covariance and the role of ENSO-related teleconnections. Both systems exhibit modest and regionally coherent skill during spring, with SPEAR showing statistically significant SPEAR advantages over CFSv2 occurring only in region- and lead-time dependent parts of CONUS, rather than consistently across the domain, while summer skill is substantially weaker in both models, especially for upper-tail seasonal precipitation anomalies. SVD results show that both models reproduce an ENSO-like leading coupled mode, but ensemble-mean SVD produces larger squared covariance fractions than observations because ensemble averaging filters internally generated variability and emphasizes the forced SST-related signal; individual-member SVD results are closer to observations. Associated 500-hPa geopotential height and vertical-motion diagnostics indicate that both systems capture some aspects of the spring Pacific–North American teleconnection pathway, but the circulation response is weaker, displaced, or less coherent in summer. Overall, SPEAR and CFSv2 share common limitations in warm-season precipitation prediction, suggesting that improved representation of SST-forced teleconnections, regional atmospheric variability, and additional climate drivers beyond ENSO is needed to improve seasonal rainfall forecasts.