The Season Ahead
A seasonal outlook for one place, stated the only honest way — as a distribution rather than a number, and with the skill of that particular gridcell printed next to it. City outlooks are the most widely published product in seasonal forecasting and the least often qualified; the column that matters most on this page is the one that says no skill.
Cities
Temperature
Rainfall
Leaning warm
One city at a time
40 cities · 51 members · 6 monthsThe band is the ensemble: the outer edge spans the 10th to 90th percentile of the 51 members, the inner one the 25th to 75th, and the line through it is the median. The dashed line is the model's own climate for that month, moved to the present. Where the two are far apart and the band is narrow, the model is saying something.
All forty
NDJ 2027 · anomaly, probability, verdictThe first three whole months ahead, summarised. °C and mm are the median member against the model's own climate; P(warm) and P(wet) are the share of members in the top third, where 33 % is the null. The verdict columns are the reforecast: verified means the tercile forecast for that cell beat climatology over 1993–2016, no skill means it did not, and the anomaly beside it should be read as a curiosity rather than a forecast.
Africa
| City | °C | P(warm) | Temperature | mm | P(wet) | Rainfall |
|---|---|---|---|---|---|---|
| Egypt | +0.5 | 48% | no skill | — | — | arid |
| South Africa | +0.2 | 42% | no skill | +3 | 42% | no skill |
| DR Congo | +0.9 | 100% | verified | +23 | 56% | no skill |
| Nigeria | +0.7 | 93% | verified | +0 | 31% | no skill |
| Morocco | +0.9 | 57% | no skill | +4 | 37% | verified |
| Kenya | +0.3 | 54% | verified | +18 | 48% | no skill |
Asia
| City | °C | P(warm) | Temperature | mm | P(wet) | Rainfall |
|---|---|---|---|---|---|---|
| Thailand | +2.1 | 91% | verified | — | — | arid |
| India | +0.5 | 59% | verified | — | — | arid |
| India | +0.2 | 46% | no skill | — | — | arid |
| United Arab Emirates | +0.4 | 50% | verified | — | — | arid |
| Indonesia | +1.3 | 100% | verified | -11 | 33% | no skill |
| Philippines | +0.8 | 95% | verified | -115 | 3% | verified |
| India | +1.8 | 88% | verified | — | — | arid |
| China | +0.5 | 48% | verified | +22 | 57% | verified |
| Singapore | +0.8 | 99% | verified | +42 | 58% | no skill |
| Japan | +1.0 | 70% | no skill | +32 | 58% | no skill |
Europe
| City | °C | P(warm) | Temperature | mm | P(wet) | Rainfall |
|---|---|---|---|---|---|---|
| Greece | +0.5 | 52% | no skill | -7 | 28% | no skill |
| Norway | -0.0 | 38% | verified | +1 | 30% | no skill |
| Germany | +0.7 | 43% | no skill | +0 | 34% | no skill |
| United Kingdom | +0.7 | 50% | no skill | +4 | 42% | no skill |
| Spain | +0.7 | 57% | verified | +12 | 45% | no skill |
| Russia | +0.7 | 42% | no skill | +6 | 44% | verified |
| France | +1.1 | 52% | verified | +4 | 36% | no skill |
| Iceland | -0.5 | 22% | verified | -5 | 26% | no skill |
| Italy | +1.0 | 52% | verified | +9 | 38% | verified |
North America
| City | °C | P(warm) | Temperature | mm | P(wet) | Rainfall |
|---|---|---|---|---|---|---|
| United States | -0.0 | 29% | verified | +8 | 40% | verified |
| Mexico | +0.5 | 61% | no skill | -7 | 27% | verified |
| United States | -0.6 | 24% | verified | +55 | 80% | no skill |
| United States | +0.5 | 37% | no skill | +40 | 65% | verified |
| United States | +1.1 | 59% | verified | -9 | 28% | no skill |
| Canada | +1.4 | 62% | verified | +14 | 40% | no skill |
Oceania
| City | °C | P(warm) | Temperature | mm | P(wet) | Rainfall |
|---|---|---|---|---|---|---|
| Australia | +0.2 | 37% | verified | +7 | 42% | verified |
| New Zealand | -0.4 | 13% | verified | +5 | 34% | no skill |
| Australia | -0.5 | 24% | no skill | +0 | 36% | verified |
| Australia | +0.3 | 41% | verified | -6 | 30% | verified |
South America
| City | °C | P(warm) | Temperature | mm | P(wet) | Rainfall |
|---|---|---|---|---|---|---|
| Colombia | +1.8 | 100% | verified | -53 | 8% | verified |
| Argentina | -0.5 | 16% | verified | +20 | 42% | verified |
| Peru | +2.8 | 100% | verified | +61 | 97% | verified |
| Brazil | +1.8 | 100% | verified | -57 | 11% | no skill |
| Brazil | +1.3 | 76% | verified | -2 | 38% | verified |
How to read this
Four things it is notResolution
A gridcell, not a city
Every briefing names the 1° cell it actually used and how far that cell centre sits from the city — usually 30–80 km. That is the right resolution for a six-month outlook, because there is no seasonal skill at scales below it. A page implying street-level precision half a year out would be claiming something the science does not support. Coastal cities are pinned to a land cell deliberately: Lima's nearest cell is mostly Pacific, and built from it the outlook read +6 °C — true of that water, nonsense for the city.
Spread
A narrow band is not confidence
The 51 members sample the uncertainty SEAS5 knows how to represent. They cannot sample the errors the model shares with itself, so a systematic bias appears as a tight band rather than a wide one. Ensemble spread is a lower bound on uncertainty, never the whole of it — which is what the skill column is for.
Baseline
Normal means the model's normal
Anomalies here are departures from SEAS5's own 1993–2016 reforecast climatology, shifted to the present — not from an observed 1991–2020 normal. A coupled model drifts, and measuring it against itself cancels the drift. It also means these numbers are not interchangeable with the ones your national service publishes.
Rainfall
Usually the honest answer is no
Far fewer cities carry a verified rainfall forecast than a verified temperature one, and in the driest places the tercile is not even a category — below 5 mm a month the gap between "below" and "above normal" is under two millimetres, so those cities are marked arid and no rainfall number is shown.