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There is no single score that can prove an AI weather forecast is better than a traditional numerical weather prediction (NWP) model. A defensible comparison uses the same forecast cases, initial conditions, lead times, variables, regions and verification data, then reports several deterministic and probabilistic measures. Results must also be separated by lead time, geography, season and event type.
Recent ECMWF evaluations illustrate why scope matters. ECMWF reported that its Artificial Intelligence Forecasting System (AIFS) had 5–15% lower errors than its physics-based Integrated Forecasting System (IFS) for most assessed medium-range surface and upper-air variables, while AIFS tended to under-predict tropical-cyclone intensity. Those findings apply to the named versions, period, variables and evaluation design—not to every AI forecast system.
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What “better” should mean in a weather-forecast comparison
AI weather systems learn relationships from historical analyses, observations and model data. Traditional NWP systems solve equations describing atmospheric physics, with observations assimilated to create initial conditions. In practice, both categories can include post-processing, bias correction and ensembles, so the label alone does not determine forecast quality.
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“Better” can mean several different things:
- Smaller average error in a continuous variable such as temperature, pressure or wind.
- More accurate probabilities for rain, storms or other binary and categorical events.
- Better discrimination between events that occur and those that do not.
- Better calibration, meaning that a stated probability occurs at approximately that frequency.
- More accurate placement and structure of precipitation systems.
- More useful warnings for high-impact or rare events.
- Stable performance when the system is moved to a different region, season or event regime.
A system can improve one property while degrading another. For example, reducing mean error does not guarantee correct event timing, realistic ensemble spread or useful cyclone-intensity warnings.
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Define the forecast task before collecting scores
Specify initialization and valid times
Record the date and time at which each forecast began, every lead time being evaluated and the valid time being verified. A 24-hour forecast, a seven-day forecast and a 15-day forecast are different tasks. Mixing them into one average can hide where a model gains or loses skill.
Name the variables, grid and domain
State the exact variable, level or accumulation period, units, horizontal grid and geographic domain. “Temperature accuracy” is incomplete without saying whether the comparison uses near-surface temperature, an upper-air level, station observations or gridded fields. Precipitation should identify the accumulation window, such as 24-hour totals, and whether verification is point-based or spatial.
Separate forecast types
Deterministic forecasts provide one value for each location and time. Ensemble or probabilistic forecasts provide a distribution or event probabilities. Medium-range prediction, sub-seasonal guidance and short-range severe-weather warnings answer different questions and should not share a headline score without a clear rationale.
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For event verification, specify the threshold and window before scoring. Examples include at least 10 millimetres of precipitation in 24 hours, a wind threshold, or a tropical cyclone reaching a defined intensity. Changing the threshold after seeing results creates an optimistic assessment.
Make the two systems comparable
Use identical cases and valid times
Both systems should forecast the same initialization dates and be verified at the same valid times. Report missing forecasts rather than silently removing difficult cases from one system. A hindcast period, start-date schedule or ensemble size that differs between systems can change the ranking even when the models themselves are unchanged.
Document versions and configuration
Record the model version, resolution, data-assimilation or initialization method, ensemble configuration, post-processing and any downscaling. Operational systems change frequently; a result for one release is not automatically a result for a later release.
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Choose and disclose the verifying reference
Verification may use direct observations, an analysis or a reanalysis. ECMWF’s AI Weather Quest uses near-real-time ERA5T. That reference is practical and consistent, but it can favor systems with similar model dynamics, parameterizations or data-assimilation characteristics. Where feasible, repeat key results with independent observations or an alternate analysis and label the reference in every table and figure.
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| Item | What to record | Why it changes interpretation |
|---|---|---|
| Forecast cases | Initialization dates, valid times and missing runs | Different samples can make one system appear better by chance. |
| Lead time | Each lead, rather than one pooled average | Skill usually changes substantially with forecast horizon. |
| System | Model name, version, resolution and configuration | Operational updates can change errors and biases. |
| Variables | Variable definition, level, units and accumulation | Scores are not interchangeable across quantities. |
| Region and season | Domain, latitude band, season and event subset | Global averages can conceal regional weaknesses. |
| Reference | Observation network, analysis or reanalysis and its version | Reference errors and structural similarity affect scores. |
| Baseline | Climatology, persistence or a named operational forecast | A skill score has no meaning without its comparator. |
| Ensemble treatment | Member count, sampling correction and aggregation | Finite ensembles can bias probabilistic comparisons. |
| Post-processing | Calibration, bias correction and spatial downscaling | It can materially improve a forecast independently of the core model. |
Use several metrics, because each answers a different question
RMSE and MAE measure typical continuous error
Root mean square error (RMSE) and mean absolute error (MAE) summarize the difference between forecast and verifying value in the variable’s units. RMSE gives disproportionately more weight to large misses; MAE is easier to interpret as a typical absolute error. State the averaging period, domain and units. A lower score is better, but the two metrics can rank systems differently when one has occasional large failures.
Bias reveals systematic over- or under-prediction
Mean error, or bias, shows whether forecasts are consistently too high or too low. A bias near zero can result from positive and negative errors canceling out, so it must be read alongside RMSE or MAE and event timing. A forecast with a small mean bias may still have poor storm placement or unrealistic spread.
Skill scores require an explicit baseline
Report improvement relative to a named reference such as climatology, persistence or another forecast system. A percentage improvement without the baseline, sample and metric is not reproducible. Include the baseline’s construction period and any regional or seasonal conditioning.
CRPS evaluates a full predictive distribution
The continuous ranked probability score (CRPS) assesses how well an ensemble or probability distribution represents the verifying outcome. It rewards concentration near the observation while penalizing distributions that are too broad or miss the observation. For finite ensembles, use a fair CRPS or fair CRP skill score when comparing systems with different member counts; the correction prevents ensemble size alone from determining the result.
Brier scores test event-probability accuracy
Brier-based scores evaluate probabilities for binary or categorical events. A forecast that assigns 70% probability should be rewarded when the event occurs and penalized when it does not. Fair forms can account for finite ensemble size. Report the event threshold, base rate and decomposition or complementary diagnostics when possible.
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ROC AUC measures discrimination, not calibration
Receiver operating characteristic area under the curve (ROC AUC) measures whether higher forecast probabilities tend to be assigned to cases in which an event occurs. It does not show whether the probabilities are numerically reliable. A system can rank events well while being systematically overconfident, so pair ROC AUC with reliability analysis.
Reliability diagrams test calibration
Group forecasts by issued probability and compare each group’s average probability with the observed event frequency. A well-calibrated 40% category contains the event about 40% of the time over a sufficiently large sample. Small ensembles and rare events produce noisy bins; show sample counts or uncertainty rather than treating a jagged diagram as proof of failure.
Use precipitation measures that respect spatial displacement
Point-by-point grid scores can punish a forecast for placing a rain band a few grid cells away even when the structure is useful. Complement them with neighborhood measures that allow nearby precipitation and object-based measures that evaluate coherent rain or storm objects. SEEPS, used by ECMWF, scores 24-hour accumulated precipitation in locally climatology-based dry, light and heavy categories, allowing station climate differences to be represented. A 2025 U.S. comparison of GraphCast and NWP precipitation used grid-based, neighborhood and object-oriented measures against ERA5 and Stage IV analyses; the methodological lesson is to report the spatial metric and reference rather than relying on one point score.
Quantify uncertainty instead of declaring a winner from a point estimate
Every score is estimated from a finite sample. Use confidence intervals, bootstrap intervals or another appropriate uncertainty method, and state whether a difference is statistically significant. The uncertainty must reflect temporal dependence, geographic aggregation and the event’s rarity where relevant.
Ensemble size and hindcast design deserve special treatment. Two systems with different member counts can have different apparent CRPS or Brier performance even if their underlying distributions are similar. Use fair corrections where available, disclose the number of members and avoid presenting an uncorrected comparison as a pure model-quality difference.
Statistical significance is not the same as practical value. A tiny improvement can be significant with a very large sample but irrelevant to a warning service; a large improvement for a rare event may remain uncertain because few cases were available.
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Break results down by the situations users actually face
Lead time
Plot or tabulate scores at each lead. A system may lead at days two to five and lose that advantage by day ten, or improve upper-air fields while showing little gain near the surface. Avoid a single “up to 15 days” value that hides the curve.
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Variable and level
Separate surface temperature, pressure, wind, humidity, precipitation and upper-air variables. Different variables respond to different model errors and may require different spatial or probabilistic metrics.
Region and season
Show continents, oceans, latitude bands and seasons separately when sample size permits. Global averages can conceal a regional weakness that matters to a national forecasting service or a local warning agency.
Event type and impact
High-impact and rare events need event-specific evaluation. For tropical cyclones, assess track and intensity separately. For heavy rain, assess threshold exceedance, placement, timing and affected area. If the forecast is used for flooding, energy or aviation, connect meteorological scores to the relevant impact thresholds instead of assuming a global average error represents user value.
Robustness and physical consistency
The World Meteorological Organization identifies robustness, transfer to other regions, fairness and consistency with physical principles as evaluation dimensions for data-trained systems. These are questions to test, not automatic failures of every AI model. Evaluate performance under unusual regimes, across geographic domains and after distribution shifts, and inspect whether outputs violate basic constraints such as impossible values or implausible spatial and temporal behavior.
What the ECMWF AIFS-versus-IFS results actually show
ECMWF’s 8 May 2026 news summary of its 2025 evaluation reported that AIFS errors were 5–15% lower than IFS errors for most assessed surface and upper-air variables in the medium range, out to 15 days. The same summary said AIFS improved tropical-cyclone location and track prediction but tended to under-predict cyclone intensity.
ECMWF’s “Forecast performance 2025” report gives additional operational context: AIFS Single v1 entered service on 25 February 2025, and AIFS-ENS launched on 1 July 2025. That report describes typical 5–15% medium-range error reductions for most assessed parameters and reports about a 10% medium-range precipitation improvement in its machine-learning-versus-physics-based forecast comparison. The report also notes larger tropical-cyclone intensity errors associated with a weak-intensity bias.
The May 2026 summary separately describes a 0.5–1% precipitation forecast-skill improvement for IFS. That figure refers to IFS improvements in the 2025 IFS evaluation; it is not another estimate of AIFS’s advantage and must not be merged with the approximately 10% statement.
| Published statement | Scope | How to report it responsibly |
|---|---|---|
| 5–15% lower errors | ECMWF’s 2025 AIFS evaluation, summarized 8 May 2026; most assessed surface and upper-air variables, medium range to 15 days | Attribute it to ECMWF and retain the model, period, variables and lead-time limits. |
| Improved cyclone location and track | Same ECMWF operational assessment | Do not generalize track gains to intensity or to every cyclone basin. |
| Under-predicted cyclone intensity | Same assessment; also described as a weak-intensity bias in the 2025 performance report | Report as a specific failure mode alongside track results. |
| About 10% precipitation improvement | ECMWF’s “Forecast performance 2025” ML-versus-physics-based comparison | Keep the report’s comparison context; do not substitute it for the separate 0.5–1% figure. |
| 0.5–1% precipitation skill improvement | May 2026 summary of the 2025 IFS evaluation | Identify it as an IFS improvement, not an AIFS-versus-IFS result. |
These findings demonstrate why model names, versions, variables, metrics, regions, reference data and periods belong in every headline result. They do not establish a universal winner between AI and NWP.
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A reproducible evaluation workflow
- Write the evaluation question. For example: “Which system gives better 24-hour precipitation probabilities over the U.S. during winter at leads of one to seven days?” Avoid starting with a model-versus-model slogan.
- Freeze the case list. Publish initialization dates, valid times, region, season and event definitions. Keep the same cases for both systems.
- Archive the exact forecasts. Save model version, resolution, ensemble members, initialization data and post-processing settings. Record missing or failed runs.
- Select the verifying reference. Use observations, an analysis or reanalysis appropriate to the variable, and document its version, coverage and known limitations.
- Establish baselines. Include climatology or another explicit reference forecast. For a skill score, show the baseline score and formula.
- Compute deterministic metrics. Report RMSE, MAE and bias with units, aggregation and uncertainty. Use the same masks and weighting for each system.
- Compute probabilistic metrics. Use CRPS or fair CRPS for distributions, Brier-based measures for events, ROC AUC for discrimination and reliability diagrams for calibration. State ensemble size and corrections.
- Evaluate spatial precipitation behavior. Add neighborhood and object-based measures when displacement or storm structure matters, and identify whether ERA5, Stage IV or another reference was used.
- Stratify the output. Publish results by lead, variable, region, season and event type. Include high-impact cases separately rather than allowing them to disappear in a global mean.
- Test sensitivity. Repeat key comparisons with an alternate reference, reasonable spatial scales or independent observation subset where possible. Note when rankings change.
- Assess uncertainty and practical significance. Give intervals, significance tests and sample counts. Explain whether an observed difference is large enough to affect a warning or operational decision.
- Publish limitations with the result. State training-data or distribution-shift concerns, physical-consistency checks, post-processing, missing cases and any reason the comparison may favor one system.
Common mistakes and how to correct them
- Comparing unmatched forecasts: Re-run the analysis on identical cases and valid times.
- Quoting a score without a baseline: Name climatology, persistence or the competing forecast and show the baseline value.
- Using only RMSE: Add bias, probabilistic scores, calibration and event or spatial measures appropriate to the use case.
- Treating reanalysis as perfect truth: Explain its analysis uncertainty and repeat with independent observations or another reference when practical.
- Ignoring ensemble size: Report member counts and apply fair finite-ensemble corrections where supported.
- Pooling all geography and seasons: Publish regional and seasonal breakdowns so local failures are visible.
- Letting average error stand in for warnings: Evaluate threshold events, timing, spatial footprint and impact-relevant outcomes.
- Assuming AI output is physically valid because its score is good: Add checks for conservation-related behavior, impossible values, continuity and transfer to unfamiliar regimes.
- Mixing model releases: Pin versions and dates; an operational update can invalidate a direct comparison.
- Confusing statistical and practical significance: Report uncertainty and discuss whether the difference changes a real decision.
How to present a result so readers can judge it
A useful headline names the systems, period, lead, variables, geography, reference and metric. For example, “AIFS Single v1 had lower ECMWF-evaluated medium-range error than IFS for most assessed variables in the 2025 sample” is informative. “AI beats physics” is not.
Put the full comparison in a table with columns for initialization and lead, model/version and configuration, variable and region, reference and baseline, metric and ensemble-size treatment, score with uncertainty, event-specific strengths or failures and post-processing. Show both absolute scores and relative skill where possible. Keep figures for track, intensity, precipitation placement and calibration separate so a strength in one dimension cannot obscure a weakness in another.
The final interpretation should answer three questions: what improved, under which conditions, and what still failed or remains uncertain. That framing is more useful than a universal ranking and remains valid as operational systems are updated.
The Bottom Line
Evaluate AI and traditional weather forecasts as matched, versioned experiments—not as a contest decided by one global number. Use an appropriate reference, multiple deterministic and probabilistic metrics, fair ensemble treatment, regional and event-level breakdowns, and explicit uncertainty. ECMWF’s AIFS results show meaningful medium-range gains alongside a tropical-cyclone intensity weakness; the correct conclusion is scoped performance, not a universal AI victory.
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