Global Extreme Weather and Climate Change Dashboard

Methodology

The IPCC detection standard this dashboard applies throughout, the Benjamini-Hochberg multiple-comparisons correction across its 57 headline variables, each variable's reliable trend window and known coverage limits, a data-currency and per-continent coverage table, and a per-variable methodology note for every phenomenon tracked here -- in that order below. A downloadable PDF version bundles all of this with a full variable/source directory and bibliography.

Publicly available data, funded by taxpayers and governments around the world, makes this site possible. Open science serves everyone.

Detection and Attribution

This dashboard tracks detection only, following the IPCC framework the sibling US dashboard uses (Glossary, AR5/AR6/SR15, "Detection and Attribution"). The homepage defines both terms and the combined IPCC-likelihood-plus-magnitude "detected change" standard this site applies throughout.

Testing 57 variables at once: correcting for chance across the full set

Testing 57 variables independently at IPCC's flat 10% example threshold (p<0.10 in the Mann-Kendall test — see Detection and Attribution above) means chance alone produces some positive results even when no underlying change exists: a 10% per-test false-positive rate yields several "significant" results out of 57 tests by chance alone, regardless of anything real happening in the climate system. This site corrects for that directly rather than only disclosing it. Of the 57 headline variables, 42 carry enough data to test at all (the rest sit under the 30-year minimum record length, excluded before significance even enters the picture). Every chart, stat tile, and summary table on this site ranks those 42 p-values together and applies a Benjamini-Hochberg false-discovery-rate correction at IPCC's 10% standard: each result has to clear a bar that tightens the more other results test more strongly, rather than the same flat 10% cutoff no matter how many tests ran. A flat, uncorrected p<0.10 threshold alone would pass 23 of these 42 results; the corrected bar passes 20. This correction applies to each variable's own default view specifically — interactively adjusting a chart's time window or switching to a different selectable series reverts that one chart to the flat IPCC standard, since the correction's ranking only holds across the one fixed set of 42 headline results it was computed from, not an arbitrary exploration away from it. The magnitude-vs-variability check described above still applies on top of this, independently: a result has to clear both bars to count as a detected change here.

Many of these 57 variables also measure correlated aspects of the same underlying phenomenon (five different drought severity thresholds against the same precipitation record, for instance) rather than fully independent draws. Benjamini-Hochberg's false-discovery guarantee holds under independence or positive dependence between tests, which correlated variables like these satisfy -- worth stating plainly rather than assuming this complication away. The Every Variable page lists a still larger number of rows by additionally breaking out every selectable series (severity tier, region, metric) within each of these 57 variables, at the flat, uncorrected threshold -- those rows are alternative views of the same 57 corrected headline tests above, not additional independent trials to correct for separately.

3 results currently change outcome under this correction -- clearing IPCC's flat 10% example threshold on its own, but not the multiple-comparisons-adjusted bar:

• Cold Extremes — Frost Days (Extreme Cold) -- p=0.053, needed p<0.0500 at rank 21 of 42.
• Flooding — River Discharge High-Flow Extent (Flooding) -- p=0.070, needed p<0.0524 at rank 22 of 42.
• Extratropical Cyclone Intensity — Minimum Pressure — Asia (Winter Storms) -- p=0.083, needed p<0.0548 at rank 23 of 42.

The full ranked set, every headline variable's own p-value against the threshold it actually has to clear:

Reliable trend windows, and excluding the in-progress year

Every chart on this site shows only its reliable-trend-window data — years before a variable's network, model, or satellite record reaches meaningful global coverage drop out entirely, rather than sitting past a default so widening the window control could pull them back in. Each variable's reliable-start year rests on a specific basis: a station-count curve crossing a threshold, a documented satellite-era reanalysis-quality break, or an aircraft-reconnaissance-era record's beginning — see each variable's methodology note below for the evidence behind its start year. No chart shows the current, in-progress calendar year as a complete data point either: a partial season or year would otherwise appear as an artificially low final point, so every series ends at the most recently complete year.

Coverage limits — where this site falls short of literally global

Several variables cover less than the whole globe, or measure a related but different quantity from what the name suggests. Each variable page states its coverage limit; this list collects them in one place:

• Extratropical cyclone intensity covers the Northern Hemisphere winter season (October–March) only, despite the underlying TRACK catalog's "global climatology" title. The catalog also pre-filters for severity (64% of tracks already qualify as "bomb cyclones," against roughly 7% in an unfiltered climatology), so storm counts serve as description only, not as a trend metric, and this site does not trend them. Its per-continent breakdown covers North America, Europe, and Asia — 3 of the 6 continents the Northern-Hemisphere-winter catalog reaches; the catalog contains no data for Africa, South America, or Oceania.
• Flooding excludes 1979–1981 from both series: GloFAS's documented one-year model spin-up (Harrigan et al. 2020) leaves a residual bias in slow-draining basins, Africa especially, through the record's first three years. See Flooding.
• Cold extremes mirrors Heat Waves' scope: it builds only the overnight-low (TMIN) side (TNn, CSDI, TN10p, Frost Days), with no TX10p (cold days by daytime high) companion, matching Heat Waves' construction without a TX90p companion.
• Convective instability (CAPE) uses a single 12Z UTC snapshot per day, not a full diurnal cycle — no single fixed UTC hour matches "local afternoon" everywhere at once, so the metric favors whichever longitudes sit near local afternoon at 12Z.
• Wildfire tracks two distinct metrics: GFED5 dry matter combusted (mass burned, by fire type) and GWIS burned area (hectares, MODIS-derived) — each a distinct physical quantity from the US dashboard's MTBS acres-burned metric, and from each other.
• Tropical cyclones reuses the companion global-tropical-cyclones.com site's global rollup directly rather than re-deriving it, and refreshes manually rather than on GWX's automated schedule.
• Tornadoes covers the United States only (reused directly from the sibling US dashboard) — no unified global tornado database exists beyond Europe's ESWD. The United States accounts for the large majority of the world's tornadoes (NOAA/NSSL's climatology puts the US share near 75%), so this site pairs US tornado counts with CAPE as the best available global proxy, rather than presenting CAPE as a stand-in for a tornado count.
• A flat global mean can hide opposite regional signals — this applies to every station-network or grid-based global variable on this site, not only one. Station and grid density varies: a global average weights a well-instrumented region like Europe or the US the same as a sparse one like Africa, South America, or the poles, so an increase in one continent can offset a decrease in another and net out to "no detected change" globally while opposite changes occur underneath. Where a per-continent breakdown exists (see Continents and the coverage table below), it offers a more complete read for this reason — check it before treating a global verdict as the whole picture.

Data currency — how current each variable runs, right now

Each variable page states its currency lag; this table collects them in one place, across all 57 GWX variables, checked directly against this build's data files.

Per-continent coverage

Drought, both wildfire variables, tropical cyclones (by ocean basin), flooding, and extratropical cyclone intensity (3 of 6 continents) run live per continent — see the nav's "Continents" dropdown for each landing page. Heat waves run live for Europe and North America (see below); the table below states the current status for every other variable.

Per-variable notes

Tropical Cyclones — ACE

Global (all-basin) Accumulated Cyclone Energy, from HURDAT2, IBTrACS, and real-time supplements. The reliable trend window starts in 1980, following Klotzbach & Landsea's published recommendation against pre-1980 data in global TC trend analyses. The 1980 cutoff addresses intensity-estimate accuracy, not basin coverage — raw storm counts already run stable well before 1980, but satellites through the 1970s carried only visible-light sensors, and the Dvorak technique reached global standardization only in the early 1980s. The companion global-tropical-cyclones.com site uses a 1966 start for the same underlying data, a difference in scope between the two sites' trend-reliability standards. An additional, lower-confidence "NATL+WPAC combined" series (selectable from this panel's Series dropdown, with a one-click time-period preset) extends back to 1945: both basins' aircraft-reconnaissance-era records begin then (NATL from 1944; NWPAC/JTWC's best-track record begins 1945), when combined NATL+NWPAC storm counts jump from 3–14/year pre-1944 to a stable 35–45+/year from 1945 on. These two basins track global activity closely: measured over the 1980–2025 global reliable window, NATL+WPAC combined account for about 52% of global ACE (WPAC alone about 37%, NATL alone about 15% — WPAC ranks as the single most active basin), about 45% of named-storm count, about 48% of hurricane and major-hurricane counts, and about 74% of Category 5 storms specifically (WPAC's warm pool produces most of the planet's strongest storms) — roughly half of global activity by every measure, and the majority of the most extreme events. The extended view carries caveats the 1980+ window avoids: NOAA's ongoing Atlantic Hurricane Reanalysis Project documents intensity biases even in the aircraft-recon era (Cat 1–2 systematically about 5kt high, the most intense storms undersampled), and WPAC's aircraft-fix record carries documented gaps (1952 missing entirely, 1955 limited).

Tropical Cyclones — Category Frequency, ACE per Storm, % Major, Rapid Intensification

Category frequency and ACE-per-storm reuse GTC's per-storm peak-intensity-tier and lifetime-ACE table directly — category frequency counts each storm's exact peak tier, not the cumulative "Cat 3+ or stronger" counts the ACE panel's levels show; Cat 1 and Cat 2 share one band (both fall in the 64–95kt "H" range, GTC's HURDAT2/IBTrACS convention). % major measures the major-hurricane share of hurricanes specifically, not of all named storms. Rapid intensification (a 30kt-or-greater increase in maximum wind within 24 hours, the standard NHC definition) runs from GTC's 6-hourly best-track fixes, comparing each fix only against the same storm's fix exactly 24 hours earlier, so a storm's first day, or any fix following a data gap, correctly registers no RI event. Hurricane Otis (2023) recorded a computed 24-hour wind increase of 90kt (55kt to 145kt) under this method, matching its documented record as one of the fastest tropical cyclone intensifications observed. All four metrics share the 1980 reliable window (Klotzbach & Landsea) and the manual-refresh schedule described above.

ACE-per-storm shows no detected change (Mann-Kendall p=0.35, nominally decreasing, 18% of the 90% historical range) over the same 1980–2025 window where % Major Hurricanes shows a detected change (p=0.030, +2.1 points/decade, 34% of the 90% range) — the two panels answer different questions rather than disagreeing. Comparing 1980–2002 to 2003–2025, Cat 1–3 storm counts have declined (Cat 1–2 hurricanes, for example, 25.8→21.1/season) while Cat 4–5 counts have risen (10.9→12.9 and 3.7→6.0/season). % major captures that compositional shift directly; ACE-per-storm averages across all named storms, most of them weak systems, so the ACE lost from fewer moderate hurricanes roughly offsets the ACE gained from the rarer, more extreme ones. Over the 1980–2025 global window, every one of the 5 category-frequency levels above individually clears detection (p between 0.03 and 0.0004 across NS/H/MH/IH/C5; magnitude 35–56% of the 90% historical range).

Both panels also carry NATL+WPAC-only levels (reliable back to 1945, versus 1980 for global), run over both the matching 1980–2025 window and the full 1945–2025 extended window, with mixed results that bear on how confidently the %major finding in particular should be read: NS and MH hold up as detected changes at every window length checked. H's decline and %major's increase do not survive the full 1945–2025 window — both flip to not-detected (%major's magnitude falls from 34% of the 90% range over 1980–2025 alone to 6% over the full record). IH's global-level increase never clears detection in this 2-basin subset at any window length, which places that increase in the basins NATL+WPAC excludes. C5's increase survives but weakens over the longer window (p=0.008 narrows to p=0.08, at the significance edge this site applies). None of this overturns the global 1980–2025 findings — the pre-1980 portion of the extended record carries documented intensity biases (see the ACE panel above), which could mask a recent trend as easily as reveal that one lacks durability — but the %major finding in particular remains open, not fully settled.

Heat Waves

GHCN-Daily's GCOS Surface Network (GSN, about 991 stations across 164 countries), the global analog of the US HCN network. Station-relative detection (a trailing 4-day mean temperature above each station's 90th-percentile-of-record threshold) makes this well-posed globally across every hemisphere and season, the same principle global-mean-temperature-anomaly products use. The reliable trend window starts in 1949 (station count crosses the same ~300-station bar the US network's 1895 window used, later here since WMO international cooperation only began in 1950). 2024 and 2023 rank as the two highest years in the 2006–2025 window, matching the established finding that 2024 and 2023 rank as the two hottest years on record globally (NASA GISS, NOAA, Copernicus, and Berkeley Earth all agree). GSN's international reporting lag runs materially worse than the US-only feed the sibling dashboard uses — most non-US stations report roughly a year behind, visible in this variable's falling station count over its most recent 1–2 years. The qualifying station count drops by roughly a third between 2018 and 2019 (from about 400–480 down to about 270–330) and stays down through 2025, a permanent contraction in the network's post-2018 completeness rather than ordinary year-to-year reporting lag; the pattern matches a systemic change in how a batch of international station reports flow into GHCN-Daily. A separate Europe-only index applies the same method to GSN stations within Europe (Russia included, per Natural Earth's continent boundaries), the one continent with a GSN network dense enough (150+ stations by 1949) to support its index.

Three companion metrics extend the headline index. The magnitude index(HWMId-style, Russo et al. 2014/2015) scores each qualifying station's most severe heat wave of the year by summing every day's magnitude — (a day's 4-day mean temperature minus the station's 25th-percentile annual maximum) divided by (the 90th-percentile minus the 25th-percentile annual maximum, the index's detection threshold) — both anchors drawn from the same station-relative annual-max distribution the event-count index uses, a simplification of Russo et al.'s original construction, which uses a separate calendar-day threshold and a 25th/75th-percentile scale. 2010 ranks as the single highest year in the record (1.78, more than 4 times the 1949–2025 mean), matching the documented 2010 Russian heat wave, Russia's worst on record; that same year ranks outside the top 5 on the bare event-count index (0.43), illustrating the distinction between one severe event and many mild ones this metric captures. TXx (IPCC AR6/ETCCDI standard) reports the single hottest daily high temperature observed each year at each qualifying station, averaged across stations, with no percentile threshold or base period. 2024 posts the highest TXx of the displayed window (35.6°C), matching 2024's status as the hottest year on record globally.WSDI (ETCCDI/CLIMDEX, Zhang et al. 2005) counts days in a run of 6 or more consecutive days above a station's fixed-base-period (1961–1990) 90th percentile.TN90p (ETCCDI/CLIMDEX) reports the percentage of days with an overnight low above the same fixed-base-period 90th percentile — NCA5 flags nighttime-low warming as a distinct, high-confidence signal, not a restatement of TXx. TXx, WSDI, and TN90p all share the 1949 reliable trend window.

Convective Instability (CAPE)

ARCO-ERA5, the same global reanalysis store the US dashboard's CAPE variable uses. The reliable trend window starts in 1979, marking ERA5's documented post-TOVS-satellite reanalysis-quality improvement. A single 12Z UTC snapshot per day stands in for a full diurnal cycle — no single fixed UTC hour matches "local afternoon" everywhere at once, and correcting for that would cost roughly 24 times more to assemble, the same cost as pulling all 24 hours. The day-count metrics (widespread and high-CAPE days) use the 90th percentile of the record's distribution, computed fresh each run, rather than a fixed absolute threshold: the US dashboard's fixed thresholds (10% of CONUS at moderate CAPE; 2500 J/kg for extreme) produce no signal at global scale, since global %-area-moderate never approaches 10% (the planet runs mostly ocean, desert, or polar at any instant) while global max CAPE exceeds 2500 J/kg on nearly every day (a thunderstorm occurs somewhere in the tropics on most days). Both metrics instead use percentile-relative thresholds, the same approach the Heat Waves variable uses. "Widespread instability days" counts days in the top decile of global %-area-at-moderate-CAPE; "high-CAPE days" counts days in the top decile of the single most unstable grid cell on Earth that day.

Wildfire

Global Fire Emissions Database (GFED5), dry-matter-combusted totals by fire type, via its pre-aggregated summary tables. Coverage runs 1997–2024, the full available record. GFED5's authors state that 1997–2000 uses coarser ATSR/VIRS-scaled estimates rather than the MODIS burned-area retrieval every year from 2001 on uses, and describe 1997–2000 as "not on par with data from the MODIS era"; the reliable trend window accordingly starts in 2001. Canada's boreal-forest DM for 2023 stands as the record in the series, 2.5 times the next-highest year, matching Canada's documented 2023 wildfire season. A second metric — GWIS burned area (hectares, MODIS MCD64A1-derived), via Our World in Data's public mirror of the JRC's GWIS dataset — covers 2002–2024. Africa averages about 67% of the global burned-area total, matching known savanna-fire dominance, and the global total declined about 18% from 2002–2012 to 2013–2024, matching Andela et al. 2017's documented decline in global burned area.

Extratropical Cyclone Intensity

CEDA/TRACK catalog (Gray, Volonte, Martínez-Alvarado, Harvey 2024), ERA5-driven — the same source and algorithm the US dashboard's Northeast-focused ETC intensity variable uses, here computed over each storm's whole track rather than a US-coast bounding box, an extension the already Northern-Hemisphere-wide underlying catalog supports directly. The reliable trend window starts in 1979. The single lowest minimum-pressure storm in the 1979–2022 record (genesis 1993-01-10, minimum MSLP 914.87 hPa) matches the Braer Storm, both in date and pressure, against the widely cited (about 914–916 hPa) historical record of that event. A per-continent breakdown covers North America, Europe, and Asia. Since most ETC fixes sit over open ocean rather than land, a strict landfall test would miss nearly every storm — this site instead assigns a storm to a continent when its track comes within 500km of that continent's Natural Earth landmass, and a storm can count toward more than one continent (one North Atlantic storm affecting both eastern North America and western Europe in sequence, for example). The Braer Storm reproduces in Europe's continent-filtered subset at effectively the same pressure.

Drought

A live index built directly from ARCO-ERA5 precipitation, in place of SPEIbase (the standard global-gridded-SPEI source), which has published no release since July 2024, breaking its roughly annual release cadence. Each 5°-coarsened land grid cell's 3-month trailing precipitation runs percentile-ranked against that cell's same-calendar-month history, never against an absolute precipitation normal, and classifies into USDM-equivalent D0–D4 categories, the same convention the US dashboard's drought variable uses. The reliable trend window spans the full 1979–2026 record. At the precipitation-only D2+ level, 2021 ranks as the single highest year in the record (the Horn of Africa's worst drought in 40 years began that year, alongside a severe western North America megadrought), with 2011 second (the Horn of Africa famine, the historic Texas/Southern Plains drought). A second, AED-inclusive companion series subtracts atmospheric evaporative demand (AED, estimated via the FAO-56 Hargreaves method from ERA5 temperature, a simplification of the source paper's full Penman-Monteith calculation) from precipitation before ranking, extending through the present the central finding of Vicente-Serrano et al. 2022 ("Global drought trends and future projections," Phil. Trans. R. Soc. A 380: 20210285): precipitation-only ("SPI-style") metrics show little or no significant historical global trend, while AED-inclusive metrics reveal regional drying that precipitation alone misses, as AED has risen with warming (+0.93 points precipitation-only versus +3.35 points AED-inclusive, comparing 1980–2002 to 2003–2025). Gebrechorkos et al. 2025 (Nature), co-authored by Vicente-Serrano, finds AED has increased drought severity by about 40% globally, a newer and independent source for the same mechanism. This site replicates the paper's historical-trend finding only, not its future-projection component. See the Library.

Cold Extremes

The structural counterpart to Heat Waves: four standard ETCCDI/CLIMDEX cold-side indices (TNn, CSDI, TN10p, Frost Days) built from the same GHCN-Daily GSN network and station data, using the same pooled-percentile-window and spell-duration-run logic as TXx/WSDI/TN90p, generalized to the cold side. Frost Days carries no percentile or base-period logic — an absolute TMIN<0°C count. The reliable trend window starts in 1949, matching Heat Waves' station-count threshold. San Antonio's station record shows TNn of −12.7°C and CSDI of 7 days in 2021 (against 0 CSDI days in each neighboring year), matching the documented February 2021 Winter Storm Uri cold wave (NWS-recorded low of −12.8°C).

Flooding

GloFAS (Global Flood Awareness System), ERA5-forced LISFLOOD reanalysis, subsampled to 1,074 "pour point" cells (the highest-upstream-area grid cell in each of the same 5° blocks Drought uses) — river discharge runs as a channelized quantity, so this site applies the standard gridded-hydrology "pour point" technique rather than Drought's block-mean approach. Two distinct series run alongside each other: an invented-here 90th-percentile-of-its-history "high flow" metric (continuous, about 10% of days qualify at any cell by construction), and GloFAS's published 2/5/20-year return-period discharge thresholds, classified into the same Alert/Warning/Severe tiers GloFAS's operational forecasting system uses (cumulative — a Severe day also counts as Warning and Alert). Both series exclude 1979–1981: GloFAS-ERA5's documented one-year model spin-up (Harrigan et al. 2020) leaves a longer residual bias in at least one region — Africa's pour-point cells averaged about 56% above their 1985–2025 baseline discharge in 1979, elevated fairly uniformly across all 12 months and fading only gradually through 1980–1981, consistent with a slow-draining-groundwater spin-up artifact rather than an actual flood event (other continents' 1979 values show no comparable anomaly). A single cutoff, FLOOD_INDEX_RELIABLE_START_YEAR = 1982, applies to every region and both series. Each region's highest-flood months line up with documented events: 1998 (El Niño East Africa and Yangtze River floods), 1982–83 (one of the 20th century's strongest El Niños, catastrophic South American flooding), 1988 (the Khartoum/Sudan flood), 2010 (the Pakistan floods) and 2010–11 (La Niña Queensland floods), 2019 (US Midwest flooding), and 2022–2024 (eastern Australia, Greece/Balkans, and West/Central Africa flood events). See docs/FLOODING_METHODOLOGY.md for the full construction.

Tornadoes (United States) & Severe Convective Storms

Tornado counts reuse the sibling US Extreme Weather Dashboard's pipeline directly, sourced from the NCEI Storm Events Database (reconstructed tornado tracks). The reliable trend window starts where NEXRAD-era radar and spotter coverage began detecting far more weak tornadoes than earlier decades could — an observing-network change rather than a climate signal in the counts before then. The United States records the large majority of the world's known tornadoes: NOAA/NSSL's international tornado climatology puts the US share near 75% of the global total (Canada adds about 5% more, so North America accounts for about 80%), a consequence of the Great Plains and Midwest's combination of moist Gulf air, dry continental air, and jet-stream shear. This concentration is why the Tornadoes page pairs US tornado counts with global CAPE as a proxy for severe-thunderstorm-favorable conditions, rather than presenting CAPE alone as a tornado count. See Convective Instability (CAPE) above for that variable's construction.

Loss Normalization

Not a live GWX pipeline like the variables above — a curated literature page (built on Pielke 2020's full 53-study normalization review) plus two separately sourced pie charts covering economic losses and loss of life, 1980–2025. Economic-by-phenomenon comes directly from EM-DAT's published dollar totals; economic-by-continent required reconstructing dollar figures from a %-of-GDP series (World Bank nominal GDP, current US$), since no ready-made continent-level dollar total exists — summing the reconstruction back up by phenomenon reproduces EM-DAT's totals within 1–3%. Loss-of-life comes from a separate source (the economic paper makes no fatality claims): North America accounts for over half of economic losses but under 4% of the weather-related death toll over the same window, while Africa and Asia together account for about 74% of deaths, each dominated by a different cause (African drought, Asian tropical cyclones).

Full per-variable technical detail (data sources, cost estimates, validation checks) lives in each variable's methodology document in this project's source repository (docs/*_METHODOLOGY.md).