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A decade-long California experiment found that probabilistic earthquake models can remain broadly consistent with real observations for a defined 24-hour window. It does not mean scientists can name the time or location of the next earthquake; it shows that some operational-style models can estimate short-term risk well enough to support public forecasting systems.
What the California experiment tested
The peer-reviewed study, led by José A. Bayona and conducted through the Collaboratory for the Study of Earthquake Predictability (CSEP), evaluated 27 earthquake-forecasting models in a fully prospective experiment. The forecasts covered California and targeted earthquakes of magnitude Mw 3.95 or greater.
Forecasts were issued before the earthquakes happened
From August 2007 through August 2018, each forecast covered the next 24 hours and was issued at midnight UTC. Models could use earthquakes observed before the start of each forecast, but the target-day observations were not available when the forecast was made. That separation limits hindsight and tests whether a model works in real time rather than merely fitting historical records.
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The models generated 51,625 next-day forecasts. Researchers compared those forecasts with 597 earthquakes observed during the study period, according to the Statewide California Earthquake Center (SCEC) in 2026. The long run of repeated forecasts allowed the evaluation to examine calibration and consistency, not just whether a single prediction happened to be correct.
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What nearly 600 earthquakes showed
The main result was consistency across several dimensions of forecast performance. The percentages below describe how many models were statistically consistent with the observations, not the chance that any individual forecast would correctly identify a particular earthquake.
| Evaluation dimension | Study result | What it means |
|---|---|---|
| Event counts | Approximately 95% of models matched the observed number of events | Most models produced an overall rate of earthquakes compatible with what occurred during the prospective test. |
| Spatial locations | About two-thirds of models matched the observed locations | Location performance was weaker than count and magnitude performance, showing that where activity occurs remains harder to represent. |
| Magnitude distribution | Approximately 95% of models matched the observed magnitudes | Most models reproduced the distribution of earthquake sizes above the study threshold. |
These results do not mean every model performed equally well. The authors reported sustained consistency for select versions of established operational-type models. A model can match statewide counts while still placing risk poorly in space, so the separate location result matters.
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Why prospective testing matters
Earthquake models are often developed by learning patterns from past seismicity. If researchers repeatedly adjust a model after seeing the same data used for evaluation, the apparent accuracy can be overstated. In CSEP’s prospective design, forecast rules were fixed before each target period and then scored against future observations using community-vetted statistical methods.
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Can scientists predict the next earthquake?
No—not in the deterministic sense implied by a precise warning. Bayona said in a University of Bristol statement dated October 15, 2026: “No scientific method can currently predict exactly when and where the next earthquake will occur. However, probabilistic forecasting can provide useful information about where, when and how large earthquakes are more likely over a defined period.”
A next-day forecast therefore describes changing odds for a specified area, 24-hour period and magnitude range. It cannot guarantee that an earthquake will occur, identify the exact fault segment that will rupture, or establish that a large earthquake is imminent. The California study validates the usefulness of calibrated probabilities over repeated forecasts; it does not turn those probabilities into certainty.
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How to compare earthquake-forecasting models
The study’s results point to six questions that matter when agencies or researchers compare systems:
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| Comparison axis | Question to ask |
|---|---|
| Prospective consistency | Did the model remain compatible with observations when evaluated on data unavailable at forecast time? |
| Event counts | Does it estimate the total number of earthquakes over the forecast window without systematic overprediction or underprediction? |
| Spatial consistency | Are higher-risk locations distributed in a way that agrees with observed earthquakes? |
| Magnitude distribution | Does it represent the relative frequency of earthquake sizes above the stated threshold? |
| Operational design | Can the system run repeatedly with current seismicity and produce a forecast on a defined schedule? |
| Transparency and reproducibility | Are the code, input data, forecast rules and evaluation methods documented well enough for independent benchmarking? |
On the California evidence, established operational-type models were the strongest candidates for sustained real-time use, but the findings do not justify treating every model in the comparison as interchangeable.
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What the findings enable agencies to do
- Publish calibrated probabilities. Forecast products can state the time window, geographic area and minimum magnitude instead of using unconditional language.
- Monitor calibration over time. Agencies can continue checking whether predicted counts, locations and magnitudes remain consistent as new observations arrive.
- Benchmark new systems. The released software, benchmark data and documentation provide a common way to test proposed models against a long prospective record.
- Separate risk communication from prediction claims. Public notices can explain that a higher probability represents elevated risk, not a scheduled or guaranteed earthquake.
Limits that still matter
- The experiment covered California, forecasts issued at midnight UTC, a 24-hour horizon and earthquakes of Mw 3.95 or greater. Results should not automatically be generalized to other regions, time windows or magnitude thresholds.
- Matching observed counts is not the same as identifying each event. A model may get the aggregate rate right while missing individual earthquakes or placing them in the wrong locations.
- About one-third of models did not match observed locations at the reported level, underscoring the difficulty of spatial forecasting.
- Prospective consistency over this period does not prove that a model will perform identically during every future sequence, including an unusually large earthquake or a change in the background rate.
- The study evaluates probabilistic risk estimates, not earthquake early-warning alerts issued after an earthquake has begun.
What this means for California residents
A credible next-day forecast can help emergency managers, infrastructure operators and the public understand relative short-term risk. It should be read like a weather probability: useful for planning and prioritization, but not a promise that an event will or will not happen at a particular address.
The practical lesson from the decade-long test is narrower and more useful than a claim of earthquake prediction. Some models used in operational forecasting can stay aligned with real observations when they are tested prospectively for years. That supports continued development and transparent public communication of probabilistic seismic risk while leaving exact timing and location beyond current science.
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