Global Extreme Weather and Climate Change Dashboard

Planet Earth Right Now

The THB Global Extreme Weather and Climate Change Dashboard (GWX) applies the IPCC's detection framework — the same standard its sibling site, the US Extreme Weather Dashboard, uses for the contiguous United States — globally, across 8 phenomena, on one shared statistical standard. No comparably broad, global, IPCC-aligned trend-detection dashboard exists publicly: most public climate-hazard tools cover either a single phenomenon or a single country, not consistent global detection across an entire hazard set.

Tropical cyclones, extratropical cyclones, extreme heat, cold spells & frost, river flooding, drought (hydrological and agricultural/ecological), and wildfire drive nearly all of the human and economic toll of weather and climate: EM-DAT records show they account for 99.7% of global weather/climate-related economic losses and 97.7% of weather/climate-related deaths, 1980–2025 (see Loss Normalization) — landslides are the only tracked EM-DAT weather/climate category this site doesn't cover. These same phenomena correspond to 9 of the IPCC's 33 official climatic impact-drivers (AR6 WGI Chapter 12, Table 12.1), about one in four of the full taxonomy; sea level, ocean heat and acidity, snow and ice, coastal erosion, and several others fall outside this scope, carrying a far smaller share of the direct human and economic toll.

The tiles below form a curated headline set (one variable per phenomenon); the full Every Variable page lists every variable this site tracks, including CAPE, GFED5 vs. GWIS wildfire, SPI vs. SPEI-style drought, and the additional tropical-cyclone metrics each phenomenon page's panels break out in more depth. Each variable's page states its geographic scope, data source, and known caveats — several cover less than the whole globe (see the Methodology page).

This dashboard performs detection only, not attribution. It assesses whether a change has occurred, not why -- evaluating specific causal factors (e.g. how much of a change traces to human-caused warming) requires model-based analysis this project hasn't undertaken. See the Detection & Attribution page and the Methodology page for the IPCC's definitions of the two terms.

Detection of Long-Term Change — Global

Detecting a change in climate differs from spotting a trend in a time series: detection demonstrates that a trend is unlikely to have arisen from natural internal variability by chance alone. Two checks apply here. First, this site tests each trend at IPCC's stated example threshold of below 10% (the nonparametric Mann-Kendall test), correcting for serial correlation (autocorrelation), not just tied values — a series' lag-1 autocorrelation, measured on its residuals after removing the fitted trend (raw-series autocorrelation inflates from the trend itself, so removing it first isolates the underlying persistence), reduces the effective sample size the significance test uses. Heat wave index, CAPE, and drought all show material lag-1 autocorrelation even after detrending (0.4-0.7 range); without correction, a long, low-noise, persistent record can show a "statistically significant" trend that reflects internal persistence rather than a signal. Correcting for this changes several individual p-values materially across the variables this site tracks, but flips no "detected change" verdict either direction — every currently detected trend survives the correction, and no previously undetected one newly clears the bar. Second, because a statistically significant trend can still run numerically small, this dashboard adds another check before counting a detected change: the trend's magnitude must also make up a meaningful share (threshold: at least 25%) of the variable's historical variability — a practical-significance filter, distinct from a rigorous natural-variability bound (this site lacks access to the long climate-model control runs formal detection and attribution studies use to establish a true natural-variability envelope; the 25% figure reflects this project's round choice, not an external standard). This combined standard — IPCC's likelihood criterion (now autocorrelation-corrected) plus a magnitude check on trends — defines "detected change" here. The magnitude check and the underlying IPCC-likelihood combination match the US dashboard's standard; the autocorrelation correction remains a GWX-specific refinement, not yet mirrored there.

Non-detected results also carry a third check, relevant mainly on the continent pages: whether the record runs long enough, given its noise level, to reliably detect a trend the size of the one actually estimated (Weatherhead et al. 1998's standard formula from the trend-detection literature). This matters more at continental scale: continental series run 1.6-4.3x noisier (by coefficient of variation) than the global series, tracking the sqrt(fewer, less-mutually-cancelling grid cells) prediction from basic statistics to within about 15%. This check stays descriptive, not a verdict override — it never changes whether something counts as a detected change (computing a specific non-significant result's "power" after the fact is a known statistical pitfall when used to explain away that same result) — it only adds an honest "this may reflect too little data, not a flat trend" caveat to the assessment text of results it applies to, shown or not shown per result on each variable's phenomenon page.

Each tile above shows a variable's reliable-trend-window data at a glance and its detected-change verdict — click through to that variable's phenomenon page for the full interactive chart, an adjustable time window, PNG/CSV downloads, and (where applicable) alternative series. The Methodology page documents full definitions and every per-variable caveat. The Every Variable page lists every variable this site tracks, including the ones without a tile above.