Attributing Extreme Weather to Climate Change: A Technical Decode of the NASEM (2026) Consensus Report

Attribution science is moving from journals into pricing, litigation, and disaster policy — where a warming tail carries a measurable cost.

Climate Attribution EEA / EEIA NASEM 2026 Expert Deep Dive

Probabilistic vs. storyline methods, the Fraction of Attributable Risk (FAR) and Risk Ratio (RR), intensity-based impact attribution, kilometer-scale modeling, and AI emulators — and where the science is defensible enough to stand up in a courtroom, a reinsurance model, or a loss-and-damage negotiation.

Reference source: National Academies of Sciences, Engineering, and Medicine. 2026. Attribution of Extreme Weather and Climate Events and Their Impacts. Washington, DC: The National Academies Press. DOI: 10.17226/28590.

Key Takeaways at a Glance

• Two subfields: Extreme Event Attribution (EEA) asks whether climate change altered the frequency or intensity of a hazard; Extreme Event Impact Attribution (EEIA) asks how much of the resulting mortality, damage, or loss is attributable to climate change.
• Confidence is uneven: Highest for temperature extremes and large-scale heavy rainfall (thermodynamically driven); lowest for severe convective storms (governed by fine-scale processes unresolved in global models).
• The methodological headline for impacts: The most defensible impact attribution for an individual event now uses intensity-based methods — not FAR-based frequency scaling.
• The compute frontier: Kilometer-scale simulations plus AI emulators are the committee's proposed route to the massive ensembles needed for robust attribution of complex, fine-scale extremes — but emulators interpolate; they cannot be trusted to extrapolate to record-shattering, out-of-distribution events.
• The legal caveat: The FAR > 0.5 “more likely than not” threshold is a contested heuristic, not a settled rule, and event attribution is not source attribution.

1. Why This Report Matters Now

Extreme event attribution has matured from a niche research curiosity into an operational discipline. Where the field once produced retrospective studies months after an event, dedicated groups now release rapid assessments within days of a disaster unfolding. The NASEM (2026) consensus report is the first full re-assessment since the National Academies' 2016 study, and it arrives at a moment when attribution findings are no longer confined to journals — they are cited in tort litigation, regulatory filings, constitutional cases, and international loss-and-damage negotiations.

The report draws a clean conceptual boundary that every downstream user — actuary, litigator, planner, or data engineer — needs to internalize:

EEA — Event Attribution

Quantifies whether, and by how much, anthropogenic climate change altered the probability or magnitude of the physical hazard itself (the heat wave, the rainfall total, the wind field).

EEIA — Impact Attribution

Translates that hazard change into attributable consequences — excess deaths, structural damage, crop loss, economic cost — at the intersection of hazard, exposure, and vulnerability.

Framing note. The report stresses that "attribution" here does not imply deterministic cause and effect. No credible study claims an event could not have happened without human influence. The claims are probabilistic and conditional — a distinction that is decisive when these findings enter a legal or actuarial context.

2. The Two Pillars of Event Attribution

Every EEA study follows a four-step workflow: define the event (spatial/temporal boundaries and the metric of interest), design the counterfactual (the "world that might have been" without human influence), compare the actual and counterfactual worlds, and communicate a formal attribution statement with quantified uncertainty. The divergence happens in how the actual and counterfactual worlds are constructed and compared.

2.1 Probabilistic (Risk-Based) Attribution

The probabilistic approach treats the event as an exceedance of a threshold for a class of similar events — for example, "the hottest day of the year exceeding 40°C" or "single-day rainfall above 50 cm." A statistical model (commonly a generalized extreme-value distribution) is fitted to observations, with parameters that shift as a function of a warming covariate such as global mean surface temperature or CO₂ concentration. The core output compares the event's return period in today's climate against the counterfactual.

The Two Core Metrics

Risk Ratio (RR)	RR = pa / pc	Ratio of event probability in the actual climate (pa) to the counterfactual climate (pc). RR > 1 means climate change made the event more likely; RR < 1 means less likely.
Fraction of Attributable Risk (FAR)	FAR = 1 − pc / pa	The share of the event's likelihood attributable to human influence. A FAR of 0.75 implies three-quarters of the event's probability is anthropogenic.

This is the framework the World Weather Attribution initiative operationalizes for rapid, near-real-time studies. Its strength is that observation-plus-existing-simulation analyses can run on modest compute. Its weakness is structural: probabilistic estimates are sensitive to event definition, and observations are typically insufficient to validate model-derived FAR and RR values — so model agreement is necessary but not sufficient for confidence.

Under the Hood: Non-Stationary Extreme Value Fitting

The RR and FAR formulas above hide the statistical machinery that actually produces p_a and p_c. Because extremes live in the tail, analysts do not fit a normal distribution — they fit an extreme value distribution to block maxima (Generalized Extreme Value, GEV) or to threshold exceedances (Generalized Pareto, GPD). The crucial modern refinement is non-stationarity: rather than assuming a fixed distribution, the parameters are allowed to drift as a function of a warming covariate — typically smoothed Global Mean Surface Temperature (GMST) or CO₂ concentration.

Technical Deep-Dive: The GEV with a GMST Covariate

A non-stationary GEV lets the location parameter (and sometimes the scale) shift with global warming, so the whole distribution translates rightward as GMST rises:

$$F\bigl(x;\,\mu(\text{GMST}),\,\sigma,\,\xi\bigr)=\exp\left\{-\left[1+\xi\left(\frac{x-\mu(\text{GMST})}{\sigma}\right)\right]^{-1/\xi}\right\}$$

Here \(\mu\) is the location (where the distribution sits), \(\sigma\) the scale (its spread), and \(\xi\) the shape (how heavy the tail is). Making \(\mu\) — commonly modelled as \(\mu(\text{GMST}) = \mu_0 + \alpha\cdot\text{GMST}\) — a function of the warming covariate is what turns a static return-period estimate into an attribution statement: the same magnitude event is read off two fitted distributions, one at today's GMST and one at the counterfactual GMST.

Model Limitations: Getting the Counterfactual Clean

Covariate choice. A single GMST covariate does not automatically isolate the anthropogenic signal. Multi-decadal internal variability — ENSO, the Atlantic Multidecadal Oscillation, the Pacific Decadal Oscillation — can project onto short records and bias FAR/RR if left unaccounted for. Common mitigations include smoothing GMST over multiple years, using multi-covariate regressions, or conditioning on the ocean state via prescribed-SST ensembles.

Competing forcings. Lumping all anthropogenic influence into one warming covariate can also mask the opposing effect of anthropogenic aerosols, whose cooling has historically suppressed warming and altered rainfall in aerosol-heavy regions such as South and East Asia. Isolating greenhouse-gas-only from aerosol-only responses through single-forcing ensembles — the hist-GHG and hist-aer experiments of the CMIP6 DAMIP (Detection and Attribution Model Intercomparison Project) — is one established way to unmask the true greenhouse signal where the two forcings compete. Attribution results are only as clean as the covariate that defines the counterfactual.

2.2 Storyline (Conditional) Attribution

The storyline approach — rooted in Shepherd's framing of a "physically self-consistent unfolding" of events — asks a different question: given the specific dynamical setup that actually occurred, how did anthropogenic warming (chiefly its thermodynamic component) change the event's characteristics? Rather than estimating the shifting probability of a class of events, it conditions on the observed circulation and isolates how much worse the same weather pattern became in a warmer world.

Techniques include event-specific hindcasts, pseudo-global-warming "delta" experiments (removing the three-dimensional climate-change signal from initial and boundary conditions), and spectral nudging, which constrains the large-scale circulation toward reanalysis so the thermodynamic response emerges through model physics alone — sharply reducing internal-variability noise and the number of simulations required.

Best for thermodynamically dominated extremes (heat, large-scale heavy rainfall) where long homogeneous records exist and the question is explicitly about changing likelihood. It is the natural fit for rapid, operational attribution and for framing statements about how much more frequent an event has become. It struggles with events whose dynamics are poorly resolved or whose magnitudes fall far outside the historical distribution.

Best for dynamically complex events — tropical cyclones, compound events — that resist clean probabilistic framing. By conditioning on the observed circulation, it can constrain uncertain dynamical dimensions and produce physically consistent "how much worse" statements. It is also the natural partner for process-based impact modeling. Its results are conditional on the chosen event and design decisions, so they answer a narrower but often more decision-relevant question.

A lighter-weight branch of the storyline family avoids running new simulations altogether by searching the observational/reanalysis record for days whose large-scale circulation resembles the event of interest. Circulation (flow) analogues compare the event against dynamically similar days drawn from an earlier, less-warmed baseline period versus a recent period, isolating the thermodynamic change while holding the flow pattern roughly fixed (e.g. Faranda et al., 2022; Terray, 2021). Constructed analogues build a synthetic best-match state as a weighted combination of historical patterns. Both are fast and observation-grounded, but they share a hard ceiling: for record-shattering events, no close analogue exists in the historical archive, so the method runs out of comparison states precisely when the stakes are highest. This is one reason machine-learning–assisted analogue search and dynamical adjustment (as explored around the 2021 Pacific Northwest heat dome) have become an active research front.

Not contradictory — complementary. The report is emphatic that probabilistic and storyline results for the same event are two answers to two different questions (one about the class of events, one about the specific dynamical realization). It recommends applying multiple approaches to the same event to increase robustness.

3. Confidence Is Not Uniform: A Per-Event-Type Breakdown

The single most useful output for practitioners is the committee's assessment that attribution confidence varies dramatically by hazard class. The gradient tracks a simple physical logic: the more an event is driven by the robust thermodynamic response to warming (a warmer atmosphere holds more moisture, temperature distributions shift), the higher the confidence. The more it depends on fine-scale dynamics that coarse global models cannot resolve, or on sparse observational records, the lower the confidence.

Event Type	Attribution Confidence	Dominant Driver	Principal Constraint	Relevance to Türkiye
Extreme Heat & Cold	Highest	Thermodynamic (direct temperature shift)	Metric sensitivity (dry vs. wet-bulb); humid-heat still emerging	High — intensifying Mediterranean/SE Anatolia heat
Large-Scale Heavy Rainfall	High	Thermodynamic (atmospheric moisture ↑)	Convective vs. large-scale distinction	High — Black Sea flash floods (e.g. 2021 Bozkurt)
Tropical Cyclones	Moderate	Mixed thermodynamic + dynamic	Track/intensity dynamics; better suited to storyline methods	Low direct relevance; rare Medicane influence
Drought	Moderate–Low	Compound (precip, evaporation, land feedbacks)	Local feedback errors; trends may sit within natural variability	Critical — Central Anatolia / Konya basin agriculture
Extratropical Cyclones	Low–Moderate	Circulation dynamics	Short/inhomogeneous records; uncertain dynamical response	Moderate — winter storm tracks, coastal surge
Wildfire (Fire Weather)	Moderate	Compound hot-dry-windy conditions	Ignition, fuel & management confound the climate signal	High — 2021 Aegean/Mediterranean fire season
Severe Convective Storms (tornadoes, hail, thunderstorm wind)	Lowest	Fine-scale convective dynamics	Unresolved by global models; heterogeneous, report-biased records	Moderate — damaging hail on agriculture (TARSİM books)

Confidence bands above are a practitioner-facing synthesis of the committee's assessment (see the report's Figure S-1 and Table 3.1). The report notes all event types sit below the 1:1 line — meaning capability can still improve across the board through better models, longer observations, and deeper process understanding.

The convective-storm caveat that matters for insurers. Severe convective storms are among the largest sources of insured loss in some markets, yet they sit at the bottom of the confidence hierarchy. U.S. tornado records since the 1950s show rising counts of weak (EF0–EF1) events and falling counts of strong (EF2–EF4) events — but much of the weak-event increase is attributable to better detection and reporting, not climate. Attribution claims about hail and thunderstorm wind should be treated with corresponding caution.

4. Impact Attribution (EEIA): From Hazard to Human Cost

EEIA builds directly on EEA output. Its governing thought experiment: what would the impacts have been without the climate-change-driven increase in hazard frequency or intensity? Critically, EEIA studies almost always hold exposure and vulnerability constant across the actual and counterfactual worlds — population distribution, infrastructure, and development are frozen so that the difference in outcomes is attributable to the hazard change alone. Two families of methods dominate.

4.1 FAR-Based Methods (Legacy — No Longer Best Practice)

The earliest impact studies simply multiplied the event's total impact by its FAR:

IFAR = Impact × FAR

This was attractive because both inputs often already existed. The Hurricane Harvey case is illustrative: applying FAR estimates of 0.31–0.80 to roughly $90 billion in direct damages produced an attributable range of $30–$72 billion, best estimate ~$67 billion. But the method rests on a flawed assumption — that the impact of an individual event scales proportionally with the change in frequency of events of that magnitude. There is little physical basis for that proportionality, and the report treats it as a misunderstanding of what FAR represents. Conclusion 5.2 is explicit on this point: for an individual event, FAR-based scaling is not the appropriate standard, and intensity-based methods are required for defensible impact attribution.

4.2 Is Intensity-Based EEIA Better Than FAR-Based EEIA?

Direct answer (Conclusion 5.2). Yes — for an individual event. The most defensible impact attribution requires intensity-based methods, which map the climate-driven change in hazard intensity to impact, rather than FAR-based methods that scale total impact by a change in event frequency. Frequency change and impact change are not the same quantity, and treating them as interchangeable misreads what FAR measures.

Intensity-based EEIA translates the climate-driven change in hazard intensity (temperature, accumulated rainfall, wind speed) into impact via one of two routes:

• Impact–response functions: curves mapping hazard intensity onto a specific outcome — heat-mortality dose–response, structural fragility curves, depth–damage relationships for flooding. These are frequently nonlinear and are typically not transferable between locations (extreme heat kills more in cooler regions with low air-conditioning penetration).
• Process-based impact models: full physical simulation chains — e.g. driving a hydrologic model of a river basin with factual and counterfactual rainfall fields, then passing the resulting flows through a depth–damage relationship to count the difference in flooded properties. These pair naturally with storyline EEA and yield the most refined (and most computationally expensive) results.

Direct vs. Indirect Impacts — A Data Engineer's Warning

Health impact functions usually capture direct effects (injury and death during the event) far better than indirect ones (chronic burden from degraded living conditions afterward). The gap is large: one analysis estimated that the average U.S. tropical cyclone generates deaths in the thousands when delayed, indirect effects are counted — against the tens typically reported from direct effects alone. Impact ledgers built only on direct counts can understate true attributable loss by orders of magnitude.

The report's cross-cutting warning for anyone consuming these numbers: uncertainty must be propagated end to end — from the original EEA analysis, through the response function or impact model, to the final loss figure. Robust, transparent uncertainty quantification is what separates a defensible loss-and-damage estimate from an indefensible one.

4.3 How Do Actuaries Fold Attribution Into Catastrophe Models?

Traditional catastrophe (CAT) models — the RMS/Moody's, Verisk/AIR, and CoreLogic engines that underpin reinsurance pricing — have historically assumed a stationary climate: the hazard catalogue is calibrated to the historical record and held fixed. That assumption is exactly what a warming climate breaks. EEA output gives actuaries a physically grounded way to perturb the hazard module rather than the historical loss record.

From Risk Ratio to the Loss Exceedance Curve

The bridge runs through the Exceedance Probability (EP) / Loss Exceedance Curve — the plot of annual loss against the probability of exceeding it, from which insurers read the Average Annual Loss (AAL) and tail metrics like the 1-in-200-year Probable Maximum Loss that drive Solvency II and rating-agency capital.

• An EEA-derived risk ratio for a peril re-weights event frequencies in the stochastic catalogue, shifting the EP curve outward at the relevant return periods.
• An intensity-based adjustment instead re-scales event severity (wind field, rainfall depth) before it hits the vulnerability/damage function — more faithful to how climate change actually loads the tail.
• The result feeds directly into solvency capital: a fatter tail raises the capital an insurer must hold against extreme years.

Caveat consistent with the confidence hierarchy discussed earlier: this re-weighting is only as trustworthy as the underlying attribution confidence. It is defensible for heat and large-scale rainfall; for severe convective storms — a dominant loss driver in some markets — the low attribution confidence means EEA-informed catalogue adjustments carry wide error bars and should be treated as scenario stress tests, not point estimates.

4.4 EEIA and the Loss & Damage Fund: Is the Science Ready?

With the UNFCCC Loss and Damage Fund operationalized after COP28 and capitalized further at COP29, EEIA is suddenly being asked to underwrite compensation, not just scholarship. The literature is openly divided on whether it is ready. One camp argues attribution is now robust enough to inform loss-and-damage allocation for well-observed perils; another cautions that the sparse observational records and missing impact–response functions across much of the Global South — precisely the regions the Fund is meant to serve — risk producing systematically lower attributable-loss estimates where data are thinnest, penalizing the most vulnerable.

The equity trap. Because intensity-based EEIA depends on locally calibrated impact–response functions, and those functions are scarcest where monitoring is weakest, a naive rollout could under-compensate exactly the communities with the least data. A pragmatic proposal in the literature is to deploy simplified, transferable intensity-based impact functions for data-poor regions as an interim standard — trading some precision for equitable coverage — while observing networks are rebuilt (see Recommendations 6.3–6.5).

5. The Compute Frontier: Kilometer-Scale Models and AI Emulators

The resolution ceiling is the field's hardest technical constraint. Current CMIP6-class global models run at horizontal grid spacings of roughly 50–300 km — far too coarse to resolve the convective processes that drive the very events (thunderstorms, convective rainfall) where confidence is lowest. Kilometer-scale modeling would close much of that gap, but running such models over climate-relevant timescales is prohibitively expensive, with severe bottlenecks in storage, transfer, and analysis.

Step 1 — Kilometer-Scale Physics

Run high-resolution, physically explicit simulations of high-impact events. These resolve the fine-scale dynamics global models parameterize away — but only for limited samples, because each run is enormously costly.

Step 2 — AI Emulators

Train machine-learning emulators on the kilometer-scale output to learn the physical patterns of high-impact events, then generate massive, dynamically consistent high-resolution ensembles cheaply — delivering the large sample sizes robust probability estimates demand.

Conclusion 4.5 (paraphrased). Prioritizing kilometer-scale simulations to train AI emulators may be the efficient route to robustly quantifying the changing probability of complex extremes that arise from fine-scale processes or cross-scale interactions — within the range of the training data. That final clause is the guardrail: emulators interpolate the physics they were shown; they do not license extrapolation to unprecedented regimes.

What are the primary limitations of AI emulators in event attribution?

AI emulators trained on kilometer-scale simulations can generate large ensembles rapidly by interpolating within the range of their training data. Their two structural limits are that generic deep-learning architectures do not inherently enforce physical conservation laws (mass, momentum, energy) without explicit constraints, and that record-shattering, out-of-distribution events fall outside the trained regime — so for the rarest, highest-impact extremes, emulators must be paired with physics-based kilometer-scale models rather than used alone.

Critical LimitsPhysics-Informed AI — and Where Emulators Break

Emulators are an efficiency multiplier, not a physics oracle. Three constraints must travel with every headline about them:

• No guaranteed conservation. Generic deep networks (CNN, ResNet, diffusion/generative emulators) do not inherently respect conservation of mass, momentum, or energy. Without physics-informed constraints or hard conservation layers, outputs can be locally plausible yet globally unphysical — a fatal flaw for extremes where the tail behaviour is the whole point.
• Interpolation, not extrapolation. Emulators learn the distribution they were trained on. A record-shattering event is by definition out-of-distribution (OOD), so the emulator is asked to predict a regime it never saw. This is the operational meaning of the report's guardrail that emulators are reliable only "within the range of the training data."
• Hybrid, not replacement. The defensible design keeps kilometer-scale physical simulations in the loop — emulators expand the ensemble cheaply inside the trained regime, while physics anchors the unprecedented tail. Treating the emulator as a stand-alone attribution engine for record events inverts the intended dependency.

Bottom line: for the record-breaking events that dominate loss and litigation, AI emulators are a complement to physical models, never a substitute.

This is where the report speaks most directly to data and ML engineers. Realizing the AI-emulator vision is not primarily a modeling problem — it is an infrastructure problem. It demands parallel investment in high-performance computing, data pipelines, and reproducible workflows. Recommendation 6.5 is aimed squarely here: centralized, community-maintained data guides and open-source, peer-reviewed algorithms — with standardized metadata on datasets, their uncertainties, and appropriate usage — are what make attribution results reproducible and auditable rather than bespoke and opaque. For a field whose outputs carry legal weight, provenance and reproducibility are not housekeeping; they are admissibility.

6. Downstream: Litigation, Risk Management, and Policy

Attribution science is increasingly consequential outside the lab. The report notes its growing role in tort cases, regulatory claims, constitutional litigation, and international climate negotiations, alongside its use in private-sector risk assessment and adaptation planning.

For Climate Litigators

The probabilistic/storyline distinction maps onto "general vs. specific causation." Intensity-based EEIA — not FAR scaling — is the more defensible basis for quantifying attributable harm in an individual case.

For Insurance Actuaries

Confidence bands by peril should shape how attribution feeds pricing and reserving. Convective-storm claims warrant heavy caveats; heat and large-scale rainfall are on firmer ground.

For Planners & Data Engineers

EEIA tools can be run on counterfactual exposure and vulnerability scenarios to stress-test adaptation strategies — not just to attribute past loss. End-to-end uncertainty propagation is non-negotiable.

6.1 A Note on Türkiye: Getting the Institutional Mapping Right

Türkiye is an instructive and encouraging case for applying these ideas, because its insurance architecture already separates risks along exactly the lines attribution science recommends. The key is to route climate-hazard attribution to the instruments built for weather risk. Türkiye's compulsory catastrophe pool (DASK/TCIP) is a purpose-built seismic instrument: its compulsory cover is dedicated to earthquakes and directly earthquake-triggered secondary perils (fire, explosion, landslide, tsunami), a focused design that has given the country one of the world's most successful mandatory earthquake schemes. Climate- and weather-driven damage such as flooding from heavy rainfall is served by a complementary set of instruments, chiefly voluntary home insurance (Konut Sigortası). This clean division of labour is a strength: it means attribution findings can be applied precisely, to the right vehicle, without disturbing the seismic pool that does its own job so well.

Where attribution adds value in Türkiye. Climate-hazard attribution maps naturally onto the voluntary property market, agricultural insurance (the TARSİM pool, which underwrites drought, hail, flood and frost risk to crops), and the reinsurance layer behind both. These weather-exposed books are precisely where attribution-informed loss-exceedance adjustments deliver the most, sharpening pricing and reserving for the perils climate change is actively reshaping. Keeping this work on the weather-exposed instruments, and letting the seismic pool stay focused on earthquakes, lets each part of the system do what it was designed to do.

Beyond insurance, Türkiye's exposure profile maps cleanly onto the confidence hierarchy set out earlier, which means much of it sits in the tier where attribution science is strongest and most immediately useful. Mediterranean and southeastern heat extremes and Black Sea heavy-rainfall/flash-flood events fall in the higher-confidence, thermodynamically driven tier where probabilistic EEA is on firm ground. The August 2021 Bozkurt (Kastamonu) flood, where roughly two-thirds of the annual rainfall fell in 48 hours and the Ezine stream overwhelmed the town, is a textbook candidate for rigorous attribution, made more tractable by a Black Sea surface then running several degrees above its seasonal average. Central Anatolian agricultural drought sits in the more demanding compound-feedback tier, where storyline methods add particular value and where investment in attribution capacity would pay off most.

The most instructive case, however, is compound and cascading risk. Five weeks after the 6 February 2023 earthquakes, torrential rain on 14–15 March 2023 reached Şanlıurfa and Adıyaman, provinces still in recovery, with 24-hour totals above 100 mm (a large fraction of the annual average in this semi-arid region), affecting some of the container and tent settlements sheltering earthquake survivors. This is precisely the preconditioned, cascading hazard the report identifies as scientifically the most challenging and the most consequential: the impact reflects not the rainfall alone but its interaction with terrain, drainage, and an exposure profile transformed by a prior disaster. Türkiye's direct experience with such compounded events gives it valuable, hard-won insight, and positions it to lead in exactly the kind of intensity-based, storyline-informed impact attribution the report calls for. Isolating the climate-attributable share of such an impact rewards a compound-event storyline layered with intensity-based EEIA, rather than a single frequency-scaled figure.

Fire weather is a second compound signature relevant to Türkiye. The severe 2021 Aegean and Mediterranean wildfire season illustrates how hot–dry–windy conditions — often summarised through elevated vapour-pressure deficit (VPD) — combine into a hazard whose attributable component is best expressed as a change in the likelihood and severity of fire-conducive weather rather than of the fires themselves, since ignition, fuel load, and land management confound the climate signal. This is the report's recurring caution that impacts respond to interacting variables, not a single driver.

Practitioner note

For an operational disaster-management audience, the actionable step is coordination across the hazard-owning institutions — the meteorological service, the disaster-management authority, and the agricultural and property insurers/reinsurers — so that attribution updates hazard baselines consistently across mandates.

6.2 General vs. Specific Causation — and the Contested FAR > 0.5 Threshold

The probabilistic/storyline split maps onto a distinction courts already use. General causation asks whether a class of exposure can cause the harm — here, whether greenhouse-gas warming can produce extremes of this kind. Specific causation asks whether it did cause the particular plaintiff's harm. Probabilistic EEA speaks mainly to the former; intensity-based EEIA, by quantifying the attributable share of the specific damage, speaks more directly to the latter.

The FAR > 0.5 (RR > 2) Argument — Useful but Contested

A recurring proposal in the climate-law literature borrows from toxic-tort reasoning: civil cases turn on the preponderance of the evidence (>50% probability), so a FAR > 0.5 — equivalently a risk ratio RR > 2, meaning human influence at least doubled the event's likelihood — is said to satisfy the "more likely than not" test that the event was attributable to climate change.

Handle with care. This equivalence is analytically neat but legally contested. It conflates the probability of an event class with the probability that this plaintiff's harm was caused by the defendant's conduct; courts do not mechanically convert a FAR value into a finding of causation, and a frequency-based FAR is a weaker basis for individual harm than an intensity-based impact estimate (as discussed in the impact-attribution section above). Treat FAR > 0.5 as a rhetorical and screening heuristic, not a settled evidentiary rule.

Scope boundary — attribution ≠ source attribution. The NASEM report addresses attributing events and impacts to climate change. It does not resolve source attribution — apportioning a share of the warming (and thus the harm) to a specific emitter or industry. That second link, central to cases like Lliuya v. RWE, is supplied not by EEA but by separate carbon-majors emissions-accounting studies that trace historical emissions to individual producers; the attribution result establishes that the hazard changed, while the carbon-majors analysis proposes who pays what share. Conflating the two is the most common error in reading attribution science into liability.

The committee's forward-looking recommendations converge on standardization without premature rigidity: a common, multidisciplinary framework for EEA and EEIA; periodic peer review of routine rapid studies; sustained investment in surface-observing networks and data recovery in the Global South; and centralized data guides to support reproducibility. For a field whose outputs now carry legal and financial weight, comparability and transparency are the currency of trust.

7. An Actionable Roadmap for Türkiye

Translating the report's methodological standards into national practice is an institutional-alignment problem more than a scientific one. The sequencing below is illustrative — a way to show how attribution capability could be built across meteorological, operational, and financial mandates — rather than an official plan.

Horizon	Initiative	Illustrative Stakeholders	Operational Output
Short term 0–1 yr	National attribution working group	Disaster-management authority, meteorological service, research councils & universities	A standardized, peer-reviewable rapid-attribution protocol for domestic hydro-meteorological disasters.
Medium term 1–3 yr	Observation-network densification	Meteorological service, water & forestry directorates	Denser automated rain-gauge and soil-moisture coverage in high-risk Black Sea and Mediterranean basins — the raw material every attribution study depends on.
Medium term 1–3 yr	Insurance integration (weather-exposed books only)	Agricultural insurance pool (TARSİM), voluntary property insurers & reinsurers, sector association	Attribution-adjusted loss-exceedance (EP) curves folded into agricultural and voluntary property underwriting, the weather-exposed books where they add the most value.
Long term 3–5 yr	Compound & debris-flow modeling	Disaster-management authority, municipalities, environment ministry	Empirical impact-response functions for post-seismic debris flows and urban-drainage stress testing — targeting exactly the March-2023-type cascade.

The through-line: attribution becomes most decision-useful once it is wired into the institutions that own each hazard, and pointed at the right book of risk. The organising principle from the Türkiye discussion carries through the whole table: applying climate-hazard attribution to the weather-exposed instruments, while the seismic pool stays focused on earthquakes, lets each part of a well-structured system contribute its full value.

8. Technical Glossary

The modeled "world that might have been" — a climate state with little or no human influence — against which the actual event is compared. Built by detrending observations, using preindustrial control runs, or running natural-forcing-only simulations.

The trend-level method (distinct from individual-event EEA) that statistically matches observed long-term trends against model-derived spatial "fingerprints" of natural-only vs. natural-plus-anthropogenic forcing, isolating the human signal from natural noise.

A storyline technique that constrains a model's large-scale circulation toward observations/reanalysis throughout a run, letting the thermodynamic response to warming emerge through model physics while suppressing internal-variability noise.

A (usually nonlinear) curve translating hazard intensity into a specific impact metric — e.g. temperature-to-mortality, wind-to-damage. Central to intensity-based EEIA and generally not transferable across locations.

Events arising from interacting hazards across space and time (e.g. compound hot-dry events driving wildfire). They pose unique attribution challenges because impacts respond to multiple, interacting hazard variables over scales larger than the triggering event.

The Generalized Extreme Value (block maxima) and Generalized Pareto (threshold exceedance) distributions used to model tails. "Non-stationary" fitting lets their parameters — location \(\mu\), scale \(\sigma\), shape \(\xi\) — vary with a warming covariate (e.g. GMST), which is what enables a probabilistic attribution statement.

Observation-based storyline techniques that compare an event to dynamically similar days (flow analogues) or to a synthesised best-match state (constructed analogues), isolating thermodynamic change while holding circulation roughly fixed. They fail for record-shattering events with no historical match.

Apportioning warming — and thus attributable harm — to specific emitters or sectors. Distinct from event/impact attribution and not resolved by the NASEM (2026) report; it relies on separate emissions-accounting ("carbon majors") analyses.

References & Citation

Primary source:

National Academies of Sciences, Engineering, and Medicine. (2026). Attribution of Extreme Weather and Climate Events and Their Impacts. Washington, DC: The National Academies Press. https://doi.org/10.17226/28590

Key methodological literature referenced within the report:

• Philip, S., et al. (2020). A protocol for probabilistic extreme event attribution analyses. Advances in Statistical Climatology, Meteorology and Oceanography.
• Shepherd, T. G. (2016; 2018). A common framework for approaches to extreme event attribution; storyline methodology. Current Climate Change Reports / Proc. Royal Society A.
• Otto, F. E. L. (2017). Attribution of weather and climate events. Annual Review of Environment and Resources, 42.
• Stott, P. A., et al. (2004). Human contribution to the European heatwave of 2003. Nature, 432.
• Perkins-Kirkpatrick, S. E., et al. (2022; 2024). On the attribution of the impacts of extreme events. Environmental Research: Climate.
• Noy, I., et al. (2024). Methods for impact attribution of extreme events.
• Frame, D. J., et al. (2020). Climate change attribution and the economic costs of extreme weather events (Hurricane Harvey). Climatic Change.
• National Academies of Sciences, Engineering, and Medicine. (2016). Attribution of Extreme Weather Events in the Context of Climate Change.
• Faranda, D., et al. (2022). Dynamical-systems and flow-analogue approaches to conditional event attribution.
• Terray, L. (2021). A dynamical adjustment / constructed-analogue perspective on regional attribution.
• Young, H., & Hsiang, S. (2024). Mortality caused by tropical cyclones including delayed, indirect effects. Nature.
• Noy, I., et al. (2023) and King, A. D., et al. (2023). Contrasting assessments of attribution readiness for Loss & Damage.

Sources for the Türkiye case data cited in this article:

• August 2021 Bozkurt (Kastamonu) flood — the 48-hour total of ~420 mm (roughly two-thirds of the annual precipitation, against an August mean near 31.5 mm) is documented in peer-reviewed remote-sensing analysis: Assessment of the August 11, 2021 Kastamonu-Bozkurt Flood Disaster with Sentinel-2 Satellite Images, Northern Turkey (2022). See also Gönençgil et al., An atmospheric approach to the flood disaster in the Western Black Sea region, 10–12 August 2021, Natural Hazards and Earth System Sciences Discussions (2022), which links the event to a positive Black Sea sea-surface-temperature anomaly.
• 14–15 March 2023 Šanlıurfa & Adıyaman floods (post-earthquake cascade) — 24-hour totals of 104.5 mm (Karaköprü, Šanlıurfa) and 125.6 mm (Çelikhan, Adıyaman), per Turkish State Meteorological Service (MGM) figures reported by Copernicus Emergency Management Service — Flooding in Southern Türkiye, March 2023. Fatalities and inundation of tent/container settlements sheltering earthquake survivors reported by UNHCR Türkiye Emergency Response situation updates (16 and 24 March 2023, ReliefWeb) and AFAD via national news agencies.
• 2021 Aegean/Mediterranean wildfire season — referenced qualitatively as an example of fire-weather (hot–dry–windy / elevated vapour-pressure-deficit) conditions; not tied to a specific quantitative claim in this article.

These Türkiye examples are illustrative case context supplied by the author; they are not drawn from the NASEM report, which does not analyse Turkish events. Figures should be re-verified against the primary sources above before any operational or citable use.

Editorial note: This article is an independent technical analysis and synthesis of the NASEM (2026) prepublication consensus report. All framing, phrasing, and interpretation are original; numerical examples and conclusions are paraphrased from the source for commentary and educational purposes. Consult the original report via the DOI above for authoritative text.

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