The study evaluated NOAA’s Seamless System for Prediction and Earth System Research, known as SPEAR, and the National Centers for Environmental Prediction’s Climate Forecast System version 2, or CFSv2, across the contiguous United States from 1991 to 2022. SPEAR is the newer of the two, developed at NOAA’s Geophysical Fluid Dynamics Laboratory with an atmospheric resolution of roughly 50 kilometers, and it runs a 15-member ensemble initialized from an ensemble coupled data assimilation system that blends in subsurface ocean temperature and salinity observations. CFSv2, operational since 2011, uses a coarser atmospheric grid of about 0.94 degrees and a 24-member lagged ensemble built from forecasts launched at four different times of day. Both systems were verified against the PRISM observational dataset, a high-resolution 4-kilometer gridded precipitation product built from quality-controlled weather stations, with sea surface temperatures checked against NOAA’s OISST and circulation fields against the ERA5 reanalysis.

The researchers deployed a battery of verification metrics designed to probe different aspects of forecast quality. Percentage bias quantified how much the models systematically over- or under-predicted rainfall, while the anomaly correlation coefficient measured whether forecasts tracked the year-to-year ups and downs of real precipitation. For the first time in this context, they also applied quantile-based categorical metrics, the critical success index, probability of detection, and false alarm ratio, to assess how well the models captured wet extremes of seasonal-mean precipitation, defined as seasons exceeding the 80th and 90th percentile thresholds of the climatological distribution. Statistical significance of model differences was tested with bootstrap resampling over 10,000 iterations, ensuring that the reported advantages of one model over the other were not artifacts of a small sample of hindcast years.

The headline finding is a stark seasonal asymmetry. During spring, both models exhibit modest but regionally coherent skill, with positive anomaly correlations across the West Coast, the north-central states, and parts of the Southwest. SPEAR’s CONUS-average spring correlations ranged from about 0.25 to 0.30, compared with roughly 0.18 to 0.28 for CFSv2, and the bootstrap test showed that SPEAR’s advantage was statistically significant only at the shortest one-month lead time. By summer, skill collapsed in both systems. SPEAR’s correlations fell to between 0.2 and 0.3 while CFSv2 hovered around 0.15, and over the South-Central United States, a case-study region encompassing Kansas, Oklahoma, and Texas, summer correlations dropped to nearly zero, exposing a profound inability of either system to anticipate summer rainfall anomalies in a region where drought and deluge both carry enormous economic stakes.

The bias analysis added further texture to the picture. In spring, both models were too wet over much of the central and eastern United States, but the problem was far worse in CFSv2, whose area-averaged gridded spring bias over the full continent reached 62 to 78 percent, compared with 31 to 33 percent for SPEAR. Over the South-Central region specifically, CFSv2’s spring wet bias climbed as high as 73 percent while SPEAR remained nearly neutral. Summer flipped the sign: SPEAR showed a robust dry bias over the continent and a severe 38 to 43 percent underestimation over the South-Central region, while CFSv2’s local wet and dry errors partially cancelled in the regional mean. The authors emphasize that how bias is summarized matters enormously, because compensating local errors can hide behind a seemingly benign regional average.

Perhaps the most practically troubling results concern wet extremes. When the researchers asked whether the models could identify seasons whose precipitation landed in the top 20 or top 10 percent of the historical distribution, the answer was largely no, especially in summer. Probability of detection declined sharply from the 80th to the 90th percentile threshold, while false alarm ratios climbed toward very high values across much of the country during June through August. Crucially, the spatial patterns of detection and false alarms mirrored the bias maps: in wet-biased regions the models trigger too many threshold exceedances, inflating both hits and false alarms simultaneously, whereas dry-biased regions suppress detections altogether. High detection rates, in other words, do not guarantee reliable forecasts when they come bundled with a flood of false alarms.

To understand why the models struggle, the team turned to the physics of the El Niño-Southern Oscillation, the dominant source of seasonal predictability for North American rainfall. An empirical orthogonal function analysis confirmed that the leading mode of tropical Pacific sea surface temperature variability during the study period was the canonical eastern-Pacific El Niño pattern, explaining 31.1 percent of SST variance and correlating at 0.95 with the standard Niño-3.4 index. Observations showed that the link between this tropical signal and South-Central U.S. rainfall is strongly seasonal: robust during winter and early spring, weakening through late spring and summer as regional convection, land-atmosphere feedbacks, and the Great Plains Low-Level Jet, which funnels Gulf of Mexico moisture into the continent’s interior, take center stage. Both models broadly reproduced this seasonal ebb and flow, though CFSv2 tended to overstate the SST-precipitation correlation in many seasons.

The centerpiece of the diagnostic work was a singular value decomposition analysis coupling tropical Pacific SST to CONUS precipitation. In spring, the observed leading mode explained 34.2 percent of the squared covariance and traced a coherent pathway: an El Niño-like SST pattern driving a Pacific-North American ridge-trough couplet in the mid-troposphere, with an anomalous ridge over the northeastern Pacific and a trough over the southeastern United States that steers Gulf moisture northward. Both models captured the qualitative structure of this spring teleconnection, but their 500-hectopascal geopotential height anomalies were only 40 to 60 percent of the observed amplitude. Strikingly, the ensemble-mean versions of the models showed squared covariance fractions of 70.5 percent for SPEAR and 87.7 percent for CFSv2, far above observations, yet when the decomposition was repeated for individual ensemble members the values fell back near the observed level. The inflation, the authors show, is a statistical artifact of ensemble averaging, which filters out chaotic internal atmospheric noise and isolates the forced SST signal rather than evidence that the models are genuinely over-dominated by El Niño.

In summer, the teleconnection machinery visibly breaks down in the models. The observed leading coupled mode shifts away from the canonical equatorial El Niño pattern toward western and off-equatorial Pacific anomalies, and the associated circulation features a deep anomalous trough over the central and eastern United States paired with rising motion that feeds continental rainfall. SPEAR retained some spatial fidelity, with a pattern correlation of 0.24 against the observed height regression, but CFSv2’s pattern correlation collapsed to 0.01, indicating a fundamental displacement of the summer circulation response. Vertical-velocity cross sections told the same story: the models produced weaker, smoother, and less vertically coherent ascent and descent anomalies than observations, consistent with known deficiencies in convective parameterizations and the simulation of Rossby wave propagation. An El Niño-like SST signal alone, the study concludes, is simply not enough; the atmospheric bridge that translates ocean warmth into rain must also be simulated faithfully.

The implications reach well beyond model scorecards. Seasonal forecasts underpin drought preparedness, reservoir operations, and agricultural planning, and the South-Central United States, a transition zone between the humid Southeast and the arid Southwest, sits squarely where those decisions hurt most when forecasts fail. The authors argue that future gains will require more than better ocean prediction: improved representation of SST-forced teleconnections, regional atmospheric variability, and additional warm-season drivers such as land-surface memory, soil moisture feedbacks, Gulf moisture transport, and modes of variability beyond ENSO. Until then, the study offers forecast users a candid calibration of what to trust: spring rainfall anomalies over the southern plains deserve guarded confidence, while summer outlooks, particularly for unusually wet seasons, should be treated with the skepticism that three decades of hindcast evidence now firmly justify.

Subject of Research: Seasonal forecast skill of the SPEAR and CFSv2 climate models for warm-season precipitation over the contiguous United States and its relation to ENSO teleconnections

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

Article References: Nguyen, H., Zhang, Y., Bakhtar, A., Lu, F., & Fernando, N. (2026). Relative skills of SPEAR and CFSv2 in foretelling warm-season precipitation anomalies over the conterminous United States and potential sources of errors. Climate Dynamics, 64(9), Article 406. https://doi.org/10.1007/s00382-026-08357-z

Image Credits: AI Generated

DOI: 10.1007/s00382-026-08357-z

Keywords: seasonal forecasting, SPEAR, CFSv2, precipitation, ENSO, teleconnections, climate models, drought, Great Plains, sea surface temperature, forecast verification, Climate Dynamics