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U.S. Billion-Dollar Weather and Climate Disasters

Adam Smith

Continuing NOAA's dataset. Climate Central now stewards it with the same methodology and lead scientist, Adam Smith. Recently, Smith spoke with S&P Global about the rise of billion-dollar disasters.

Climate Central maintains this comprehensive database tracking U.S. weather and climate disasters since 1980 where overall damages/costs reached or exceeded $1 billion (including Consumer Price Index (CPI) adjustment to 2026). As of September 2026, the U.S. has sustained 447 such events, with a total cost exceeding $3.3 trillion.

CPI-Adjusted: All costs are adjusted for inflation to 2026 dollars using the U.S. Bureau of Labor Statistics' Consumer Price Index (CPI).

All years (1980–2026)

447Events
$3.3 trillionTotal Cost
17,395Deaths
Disaster Types
All types shown
Selected disaster type: Drought
Selected disaster type: Flooding
Selected disaster type: Freeze
Selected disaster type: Severe Storm
Selected disaster type: Tropical Cyclone
Selected disaster type: Wildfire
Selected disaster type: Winter Storm

1980-2026 Oklahoma Billion-Dollar Disaster Year-to-Date Event Cost (CPI-Adjusted)

Data represented in the billion-dollar disaster overview chart
SeriesMonthCumulative cost
1999January$100M–250M
1999February$100M–250M
1999March$100M–250M
1999April$100M–250M
1999May$2B–5B
1999June$2B–5B
1999July$2B–5B
1999August$2B–5B
1999September$2B–5B
1999October$2B–5B
1999November$2B–5B
1999December$2B–5B
2019January$0
2019February$0
2019March$250M–500M
2019April$250M–500M
2019May$500M–750M
2019June$2B–5B
2019July$2B–5B
2019August$2B–5B
2019September$2B–5B
2019October$2B–5B
2019November$2B–5B
2019December$2B–5B
2003January$0
2003February$0
2003March$0
2003April$0
2003May$2B–5B
2003June$2B–5B
2003July$2B–5B
2003August$2B–5B
2003September$2B–5B
2003October$2B–5B
2003November$2B–5B
2003December$2B–5B
2025January$0
2025February$0
2025March$250M–500M
2025April$750M–999M
2025May$1B–2B
2025June$2B–5B
2025July$2B–5B
2025August$2B–5B
2025September$2B–5B
2025October$2B–5B
2025November$2B–5B
2025December$2B–5B
2021January$0
2021February$500M–750M
2021March$500M–750M
2021April$2B–5B
2021May$2B–5B
2021June$2B–5B
2021July$2B–5B
2021August$2B–5B
2021September$2B–5B
2021October$2B–5B
2021November$2B–5B
2021December$2B–5B
2009January$0
2009February$500M–750M
2009March$500M–750M
2009April$500M–750M
2009May$500M–750M
2009June$750M–999M
2009July$750M–999M
2009August$750M–999M
2009September$750M–999M
2009October$750M–999M
2009November$750M–999M
2009December$2B–5B
2024January$0
2024February$0
2024March$100M–250M
2024April$500M–750M
2024May$1B–2B
2024June$1B–2B
2024July$1B–2B
2024August$1B–2B
2024September$1B–2B
2024October$1B–2B
2024November$1B–2B
2024December$1B–2B
1988January$0
1988February$0
1988March$0
1988April$0
1988May$0
1988June$0
1988July$0
1988August$1B–2B
1988September$1B–2B
1988October$1B–2B
1988November$1B–2B
1988December$1B–2B
2022January$0
2022February$0
2022March$0
2022April$5M–100M
2022May$100M–250M
2022June$100M–250M
2022July$100M–250M
2022August$100M–250M
2022September$100M–250M
2022October$100M–250M
2022November$100M–250M
2022December$1B–2B
1980January$0
1980February$0
1980March$0
1980April$0
1980May$0
1980June$0
1980July$0
1980August$0
1980September$0
1980October$0
1980November$1B–2B
1980December$1B–2B
2020January$0
2020February$0
2020March$250M–500M
2020April$1B–2B
2020May$1B–2B
2020June$1B–2B
2020July$1B–2B
2020August$1B–2B
2020September$1B–2B
2020October$1B–2B
2020November$1B–2B
2020December$1B–2B
2015January$0
2015February$0
2015March$0
2015April$5M–100M
2015May$750M–999M
2015June$750M–999M
2015July$750M–999M
2015August$750M–999M
2015September$750M–999M
2015October$750M–999M
2015November$750M–999M
2015December$1B–2B
2008January$5M–100M
2008February$5M–100M
2008March$5M–100M
2008April$100M–250M
2008May$250M–500M
2008June$500M–750M
2008July$500M–750M
2008August$500M–750M
2008September$500M–750M
2008October$500M–750M
2008November$500M–750M
2008December$1B–2B
2014January$0
2014February$0
2014March$0
2014April$0
2014May$0
2014June$0
2014July$0
2014August$0
2014September$0
2014October$0
2014November$0
2014December$750M–999M
2006January$0
2006February$0
2006March$5M–100M
2006April$5M–100M
2006May$5M–100M
2006June$5M–100M
2006July$5M–100M
2006August$500M–750M
2006September$500M–750M
2006October$500M–750M
2006November$500M–750M
2006December$750M–999M
1996January$0
1996February$0
1996March$0
1996April$0
1996May$0
1996June$0
1996July$0
1996August$500M–750M
1996September$500M–750M
1996October$500M–750M
1996November$500M–750M
1996December$500M–750M
2017January$0
2017February$0
2017March$100M–250M
2017April$100M–250M
2017May$500M–750M
2017June$500M–750M
2017July$500M–750M
2017August$500M–750M
2017September$500M–750M
2017October$500M–750M
2017November$500M–750M
2017December$500M–750M
2007January$0
2007February$0
2007March$0
2007April$500M–750M
2007May$500M–750M
2007June$500M–750M
2007July$500M–750M
2007August$500M–750M
2007September$500M–750M
2007October$500M–750M
2007November$500M–750M
2007December$500M–750M
1992January$0
1992February$0
1992March$0
1992April$5M–100M
1992May$5M–100M
1992June$500M–750M
1992July$500M–750M
1992August$500M–750M
1992September$500M–750M
1992October$500M–750M
1992November$500M–750M
1992December$500M–750M
2002January$0
2002February$0
2002March$0
2002April$0
2002May$0
2002June$0
2002July$0
2002August$0
2002September$0
2002October$0
2002November$500M–750M
2002December$500M–750M
1991January$0
1991February$0
1991March$0
1991April$0
1991May$0
1991June$0
1991July$0
1991August$250M–500M
1991September$250M–500M
1991October$250M–500M
1991November$250M–500M
1991December$250M–500M
1990January$0
1990February$0
1990March$0
1990April$0
1990May$250M–500M
1990June$250M–500M
1990July$250M–500M
1990August$250M–500M
1990September$250M–500M
1990October$250M–500M
1990November$250M–500M
1990December$250M–500M
2018January$0
2018February$0
2018March$0
2018April$0
2018May$5M–100M
2018June$5M–100M
2018July$5M–100M
2018August$5M–100M
2018September$5M–100M
2018October$5M–100M
2018November$5M–100M
2018December$250M–500M
2000January$0
2000February$0
2000March$0
2000April$0
2000May$0
2000June$0
2000July$0
2000August$0
2000September$0
2000October$0
2000November$250M–500M
2000December$250M–500M
1998January$0
1998February$0
1998March$0
1998April$0
1998May$0
1998June$0
1998July$0
1998August$250M–500M
1998September$250M–500M
1998October$250M–500M
1998November$250M–500M
1998December$250M–500M
2016January$0
2016February$0
2016March$5M–100M
2016April$5M–100M
2016May$250M–500M
2016June$250M–500M
2016July$250M–500M
2016August$250M–500M
2016September$250M–500M
2016October$250M–500M
2016November$250M–500M
2016December$250M–500M
1982January$5M–100M
1982February$5M–100M
1982March$5M–100M
1982April$100M–250M
1982May$100M–250M
1982June$250M–500M
1982July$250M–500M
1982August$250M–500M
1982September$250M–500M
1982October$250M–500M
1982November$250M–500M
1982December$250M–500M
1994January$0
1994February$0
1994March$0
1994April$100M–250M
1994May$100M–250M
1994June$100M–250M
1994July$100M–250M
1994August$100M–250M
1994September$100M–250M
1994October$100M–250M
1994November$100M–250M
1994December$100M–250M
1995January$0
1995February$0
1995March$0
1995April$0
1995May$100M–250M
1995June$100M–250M
1995July$100M–250M
1995August$100M–250M
1995September$100M–250M
1995October$100M–250M
1995November$100M–250M
1995December$100M–250M
1985January$5M–100M
1985February$5M–100M
1985March$5M–100M
1985April$5M–100M
1985May$5M–100M
1985June$5M–100M
1985July$5M–100M
1985August$5M–100M
1985September$5M–100M
1985October$5M–100M
1985November$5M–100M
1985December$5M–100M
1983January$0
1983February$0
1983March$0
1983April$0
1983May$0
1983June$0
1983July$0
1983August$0
1983September$0
1983October$0
1983November$0
1983December$5M–100M
2001January$0
2001February$0
2001March$0
2001April$5M–100M
2001May$5M–100M
2001June$5M–100M
2001July$5M–100M
2001August$5M–100M
2001September$5M–100M
2001October$5M–100M
2001November$5M–100M
2001December$5M–100M
2004January$0
2004February$0
2004March$0
2004April$0
2004May$5M–100M
2004June$5M–100M
2004July$5M–100M
2004August$5M–100M
2004September$5M–100M
2004October$5M–100M
2004November$5M–100M
2004December$5M–100M
1989January$0
1989February$0
1989March$0
1989April$0
1989May$5M–100M
1989June$5M–100M
1989July$5M–100M
1989August$5M–100M
1989September$5M–100M
1989October$5M–100M
1989November$5M–100M
1989December$5M–100M
1981January$0
1981February$0
1981March$0
1981April$0
1981May$5M–100M
1981June$5M–100M
1981July$5M–100M
1981August$5M–100M
1981September$5M–100M
1981October$5M–100M
1981November$5M–100M
1981December$5M–100M
1984January$0
1984February$0
1984March$0
1984April$0
1984May$0
1984June$0
1984July$0
1984August$0
1984September$0
1984October$0
1984November$0
1984December$0
1986January$0
1986February$0
1986March$0
1986April$0
1986May$0
1986June$0
1986July$0
1986August$0
1986September$0
1986October$0
1986November$0
1986December$0
1987January$0
1987February$0
1987March$0
1987April$0
1987May$0
1987June$0
1987July$0
1987August$0
1987September$0
1987October$0
1987November$0
1987December$0
1993January$0
1993February$0
1993March$0
1993April$0
1993May$0
1993June$0
1993July$0
1993August$0
1993September$0
1993October$0
1993November$0
1993December$0
1997January$0
1997February$0
1997March$0
1997April$0
1997May$0
1997June$0
1997July$0
1997August$0
1997September$0
1997October$0
1997November$0
1997December$0
2005January$0
2005February$0
2005March$0
2005April$0
2005May$0
2005June$0
2005July$0
2005August$0
2005September$0
2005October$0
2005November$0
2005December$0
2023January$0
2023February$0
2023March$5M–100M
2023April$1B–2B
2023May$1B–2B
2023June$2B–5B
2023July$2B–5B
2023August$2B–5B
2023September$2B–5B
2023October$2B–5B
2023November$2B–5B
2023December$2B–5B
2010January$0
2010February$0
2010March$0
2010April$0
2010May$2B–5B
2010June$2B–5B
2010July$2B–5B
2010August$2B–5B
2010September$2B–5B
2010October$2B–5B
2010November$2B–5B
2010December$2B–5B
2013January$0
2013February$5M–100M
2013March$5M–100M
2013April$5M–100M
2013May$2B–5B
2013June$2B–5B
2013July$2B–5B
2013August$2B–5B
2013September$2B–5B
2013October$2B–5B
2013November$2B–5B
2013December$2B–5B
2012January$0
2012February$0
2012March$0
2012April$100M–250M
2012May$2B–5B
2012June$2B–5B
2012July$2B–5B
2012August$2B–5B
2012September$2B–5B
2012October$2B–5B
2012November$2B–5B
2012December$2B–5B
2011January$0
2011February$5M–100M
2011March$5M–100M
2011April$500M–750M
2011May$1B–2B
2011June$1B–2B
2011July$1B–2B
2011August$2B–5B
2011September$2B–5B
2011October$2B–5B
2011November$2B–5B
2011December$2B–5B
2026January$100M–250M
2026February$100M–250M
2026March$250M–500M
2026April$500M–750M
2026May$1B–2B
2026June$1B–2B
2026July$1B–2B
2026August$1B–2B
2026September$1B–2B
AverageJanuary$5M–100M
AverageFebruary$5M–100M
AverageMarch$5M–100M
AverageApril$100M–250M
AverageMay$500M–750M
AverageJune$750M–999M
AverageJuly$750M–999M
AverageAugust$750M–999M
AverageSeptember$750M–999M
AverageOctober$750M–999M
AverageNovember$750M–999M
AverageDecember$1B–2B

Month-by-month accumulation of billion-dollar disasters for each year on record. The colored lines represent the top 6 years for most billion-dollar disasters. The dark gray line shows the average. All other years are colored light gray.

Methodology and Data Sources

As of July 28, 2025, Climate Central manages and maintains this billion-dollar disaster dataset, building upon the foundational work established by NOAA's National Centers for Environmental Information (NCEI). This dataset tracks and evaluates weather and climate events in the U.S. that have significant economic and societal impacts, providing critical insights into the growing costs of extreme weather in a changing climate. The billion-dollar disasters analysis demonstrates the economic impact of extreme weather and climate events in inflation-adjusted dollars, helping communicate the real-world consequences of climate change to communities, policymakers, and the public.

As Climate Central continues this important work, we emphasize that disaster impacts result from a combination of increased risk and climate variability. The increase in population and material wealth over recent decades contributes to rising costs, particularly as development has expanded into vulnerable areas like coasts, the wildland-urban interface, and river floodplains. Vulnerability is especially high where building codes are insufficient for reducing damage from extreme events. This increasing exposure and vulnerability helps explain why the 2010-2019 decade is far costlier than previous decades (all inflation-adjusted to current dollars). The methodology and data sets are documented in peer-reviewed literature and continue to be refined and improved.

The increasing number and cost of weather and climate disasters in the U.S. result from a combination of increased exposure (more assets at risk), vulnerability (how much damage a hazard of given intensity causes at a location), and changes in the frequency of some types of extreme weather that lead to billion-dollar disasters (National Academies of Sciences, Engineering, and Medicine. Compounding Disasters in Gulf Coast Communities 2020-2021: Impacts, Findings, and Lessons (2024); Multi-Hazard Mitigation Council. Natural Hazard Mitigation Saves (2019)). Research shows that climate variability can alter the frequency and intensity of certain types of extreme weather that lead to billion-dollar disasters — most notably the rise in vulnerability to drought, lengthening wildfire seasons in western states, and the potential for extreme heavy rainfall becoming more common in eastern states. Sea level rise can worsen hurricane storm surge flooding (Fifth National Climate Assessment (2023)).

The original methodology for this dataset was thoroughly reviewed by NCEI in 2012, when NCEI held a workshop with economic experts and worked with consulting partners to examine possible inaccuracies and biases in data sources and methodology. This review process ensured greater consistency and confidence in year-to-year comparisons. The methodology improvements are documented in the peer-reviewed article"U.S. Billion-Dollar Weather and Climate Disasters: Data Sources, Trends, Accuracy and Biases" (Smith and Katz, 2013). This research found that the net effect of all biases appears to be an underestimation of average loss, with the factor approach potentially underestimating average loss by approximately 10% to 15%, which was updated for all events, after a reanalysis of the loss data to reflect more accurate loss totals. Climate Central continues to apply these improved methodological standards.

More recently, feedback from the American Meteorological Society community has suggested incorporating smaller cost thresholds into the analysis to more completely capture the hazard risk space. Climate Central is exploring this more comprehensive scope by quantifying total, direct losses for all sub-billion events down to $100 million from 1980-present. This new research scheduled for Spring 2026 will provide analysis for hundreds of additional events and more completely describe the weather and climate event frequency and cost distribution over space, time, and by hazard.

Climate Central's disaster cost assessments use the most comprehensive public and private sector data sources available, representing the estimated total costs of these events — that is, the costs in dollars that would not have been incurred had the event not taken place. More than a dozen public and private sector data sources help capture the total, direct costs (both insured and uninsured) of weather and climate events. These costs include: physical damage to residential, commercial, and municipal buildings; material assets (content) within buildings; time element losses such as business interruption or loss of living quarters; damage to vehicles and boats; public assets including roads, bridges, and levees; electrical infrastructure and offshore energy platforms; agricultural assets including crops, livestock, and commercial timber; and wildfire suppression costs, among others. However, these disaster costs do not account for losses to natural capital or environmental degradation; mental or physical healthcare-related costs; the value of a statistical life (VSL); or supply chain and contingent business interruption costs. These estimates should therefore be considered conservative with respect to what is truly lost, but cannot be completely measured due to a lack of consistently available data. Sources include the National Weather Service, the Federal Emergency Management Agency, U.S. Department of Agriculture, National Interagency Fire Center, U.S. Army Corps, individual state emergency management agencies, state and regional climate centers, and insurance industry estimates, among others.

Links to key public sector data sources include FEMA Public Assistance, FEMA Individual Assistance, USDA Risk Management Agency Crop Indemnity, the USDA National Agricultural Statistics Service, the National Interagency Fire Center wildfire data, the National Flood Insurance Program claims data and the NOAA Storm Events Database. We also reference state insurance services such as CalFire and FLOIR, the U.S. Department of Defense disaster impact reports, the Federal Highway Administration damage reports and private property insurance data as part of this event analysis. To better understand insured and uninsured assets, we also examine insurance penetration rates for vehicle insurance, home insurance, flood insurance, crop insurance, and business coverage. As a point of comparison for robustness, the original NCEI assessment of 28 separate billion-dollar disaster events in 2023 showed that for 12 events, the damage estimates were smaller than those in other sources, such as Gallagher Re, AON, and Munich Re. For the other 16 events, the estimates fell within the range of other reports such as Gallagher Re, Munich Re and AON 2023 disaster cost reports. Climate Central continues to incorporate external peer-review processes to capture best practices and ensure methodological rigor.

Climate Central acknowledges the foundational work of NOAA's National Centers for Environmental Information (NCEI) in developing and maintaining this dataset from 1980 through the 2025 transition to Climate Central. As the new steward of this critical resource, Climate Central is committed to maintaining the scientific rigor and methodological standards established by NCEI while enhancing the dataset's utility for climate communication and public understanding of climate risks.

In the soon-to-be-updated Risk Mapping section of this site, Climate Central also incorporates socioeconomic risk information from the Census American Community Surveys, the CDC Social Vulnerability Index, and other sources that provide additional spatial context of hazard risk. For more information on risk mapping methodology, see: Smith, A., 2022: 2021 U.S. Billion-dollar weather and climate disasters in historical context and new hazard and socioeconomic risk mapping. April 2022, AMS Washington Forum, Washington, DC.

For more recent analysis of this data, see the latest summary: 2024 - An active year of U.S. billion-dollar weather and climate disasters. Climate Central continues to provide updated analysis and interpretation of this data to help communities understand and prepare for the growing economic impacts of extreme weather in a changing climate.