Extreme rain, measured on the ground
Precipitation extremes are poorly captured by models: reanalyses smooth them. This page relies on the rain gauges of 19 stations. Marker colour shows whether the latest complete month was dry or wet (one-month SPI).
- dry month (SPI ≤ −1)
- near normal
- wet month (SPI ≥ 1)
- very wet (SPI ≥ 1.5)
GHCN-Daily stations; colour: SPI-1 of the latest complete month (McKee classes).
Tokyo GHCN station JA000047662 · 1961-01-01 → 2025-08-24
Show the data as a table
Show the data as a table
Show the data as a table
| # | Day | mm |
|---|---|---|
| 1 | 1996-09-22 | 261.1 |
| 2 | 1993-08-27 | 244.1 |
| 3 | 1991-09-19 | 228.1 |
| 4 | 1966-06-28 | 225.5 |
| 5 | 2014-10-06 | 222.0 |
| 6 | 1981-10-22 | 215.0 |
| 7 | 2023-06-03 | 213.1 |
| 8 | 1989-08-01 | 195.0 |
| 9 | 1986-08-04 | 185.0 |
| 10 | 1994-04-27 | 178.1 |
| Period | Max. 30-day total (mm) | Threshold (mm) |
|---|---|---|
| 2004-10-18 → 11-08 | 814 | 395 |
| 1966-06-09 → 07-27 | 540 | 262 |
| 2019-11-04 → 11-11 | 531 | 524 |
| 2015-09-10 → 10-05 | 517 | 345 |
| 1982-09-25 → 10-11 | 464 | 338 |
| 1999-07-26 → 08-03 | 459 | 266 |
| 2016-08-21 → 09-17 | 451 | 380 |
| 1971-09-10 → 09-29 | 446 | 341 |
| 1979-10-19 → 10-22 | 445 | 387 |
| 2021-08-14 → 09-07 | 442 | 364 |
| City | 1-day record (mm) | Rx1day, latest year (mm) | Rx1day trend (mm/decade) | Complete months |
|---|---|---|---|---|
| Rome | 120 (1978) | 36 (2023) | +2.2 ± 1.6 | 95 % |
| New Delhi | 263 (1996) | 68 (2004) | +3.1 ± 9.2 | 46 % |
| Lagos | 361 (2018) | — | — | 0 % |
| Paris | 75 (2018) | 24 (2025) | +1.6 ± 0.8 | 100 % |
| Tokyo | 261 (1996) | 76 (2024) | +4.0 ± 3.4 | 78 % |
| Berlin | 106 (1978) | 21 (2025) | -0.4 ± 1.1 | 100 % |
| London | 62 (1992) | 21 (2025) | -1.4 ± 0.7 | 100 % |
| Madrid | 87 (1972) | 28 (2025) | -0.4 ± 0.8 | 100 % |
| Mexico | 163 (2012) | 36 (2017) | — | 21 % |
| Sydney | 216 (1990) | 104 (2025) | -2.6 ± 2.6 | 99 % |
| Beijing | 263 (2016) | 126 (2024) | -2.2 ± 2.9 | 90 % |
| Chicago | 174 (2011) | 47 (2025) | +0.9 ± 2.1 | 100 % |
| Jakarta | 200 (1979) | — | — | 0 % |
| Nairobi | 352 (2010) | 70 (1997) | +6.0 ± 10.0 | 60 % |
| New York | 192 (2007) | 67 (2025) | +2.2 ± 2.3 | 100 % |
| Phoenix | 84 (2014) | 50 (2025) | +0.2 ± 0.9 | 100 % |
| São Paulo | 301 (2008) | 56 (2023) | -4.4 ± 2.6 | 67 % |
| Marseille | 161 (1973) | 110 (2025) | -0.5 ± 2.0 | 100 % |
| Buenos Aires | 300 (1973) | — | — | 1 % |
Daily rain of the GHCN-Daily stations (Menne et al., 2012) since 1961, values flagged by the quality control left out. Indices of the ETCCDI expert team (Zhang et al., 2011), per calendar year: Rx1day and Rx5day (heaviest 1- and 5-day rain), R20mm (days with at least 20 mm), CDD and CWD (longest runs of dry, below 1 mm, and wet days), PRCPTOT (total of wet days) and R95p (rain of the days above the 95th percentile of 1991–2020 wet days). A year is kept only with at least 330 measured days. Persistent episodes: 30-day total above the 90th percentile of the same calendar day (1991–2020, 15-day window).
Stations rather than the ERA5 reanalysis: ERA5 reproduces monthly totals well but underestimates intense rain and its local variability (Lavers et al., 2022).
A rain gauge measures one point: a storm may miss the station and hit the next town, and a station’s records do not tell everything about a region. Sixty-year trends in extreme rain are small compared with their variability: the standard error given says so, and most are not significant taken one by one.
Changes of instrument, site or observation time can create breaks that GHCN-Daily does not correct (unlike homogenised monthly series).
- GHCN-Daily stations of the reference places (NOAA NCEI)
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Menne, M. J., Durre, I., Vose, R. S., Gleason, B. E. and Houston, T. G. (2012). An overview of the Global Historical Climatology Network-Daily database. J. Atmos. Oceanic Technol., 29, 897–910. Menne, M. J., et al. (2012). Global Historical Climatology Network - Daily (GHCN-Daily), Version 3. NOAA NCEI.
· doi:10.7289/V5D21VHZ · source page ↗
Licence : U.S. Government work, public domain (data from national services under WMO Resolution 40); cite Menne et al. (2012) · retrieved on Oct 25, 2026 · SHA-256 fb525098ce40… - Zhang et al. (2011)
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Zhang, X., Alexander, L., Hegerl, G. C., Jones, P., Klein Tank, A., Peterson, T. C., et al. (2011). Indices for monitoring changes in extremes based on daily temperature and precipitation data. WIREs Climate Change, 2(6), 851–870.
· doi:10.1002/wcc.147
Licence : Published literature; cited - Menne et al. (2012)
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Menne, M. J., Durre, I., Vose, R. S., Gleason, B. E., & Houston, T. G. (2012). An overview of the Global Historical Climatology Network-Daily database. Journal of Atmospheric and Oceanic Technology, 29(7), 897–910.
· doi:10.1175/JTECH-D-11-00103.1
Licence : Published literature; cited - Lavers et al. (2022)
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Lavers, D. A., Simmons, A., Vamborg, F., & Rodwell, M. J. (2022). An evaluation of ERA5 precipitation for climate monitoring. Quarterly Journal of the Royal Meteorological Society, 148(748), 3152–3165.
· doi:10.1002/qj.4351
Licence : Published literature; cited