How do we anticipate the weather up to 12 months ahead?

The principle in one sentence

This distinction matters. When you check the weather 5 days out, you get a real forecast from numerical atmospheric models. When you check a date 4 months away, you get a statistical estimate based on what happened at the same dates in previous years, adjusted by current seasonal model trends.

Our data sources

All our weather data comes from Open-Meteo, an open-source API that aggregates ERA5 reanalyses from the European Centre for Medium-Range Weather Forecasts (ECMWF), as well as operational models from Météo-France (ARPEGE/AROME), Germany's DWD and NOAA in the United States.

Why ERA5?

ERA5 is the world's reference reanalysis: it reconstructs the state of the atmosphere hour by hour since 1940, at a 31 km resolution, by assimilating all available observations (satellites, radiosondes, ground stations). It's the dataset used by climate researchers to establish norms.

Our analysis window covers the last 10 years. This is deliberate: including older data would introduce a growing cooling bias due to climate change, making estimates less representative of current conditions.

Three levels by time horizon

The reliability of a weather estimate decreases with time distance. We indicate this explicitly in the interface via a horizon badge. Here's what each level actually means.

How does the score work?

The score out of 10 summarises weather conditions based on your activity. It's not universal: good weather for a beach day isn't the same as for skiing. Four criteria are weighted differently depending on the selected profile.

Relative importance by profile

The four factors are weighted according to a calibration specific to each profile, reflecting the relative importance of each criterion for that activity. Here's an indicative overview of the priorities:

● = low priority · ●●●●● = dominant priority. Exact weights come from internal calibration and are not disclosed.

Ideal temperature ranges

Each profile defines an optimal temperature range. Outside this range, the temperature sub-score decreases progressively.

Verdicts

The final score is converted into a qualitative verdict: Ideal (≥ 8.0/10), Favourable (6.5–7.9/10), Acceptable (4.5–6.4/10), Off season (< 4.5/10). These thresholds are identical across all profiles.

Annual reference score by destination

For destinations in the catalogue (climate guides), each month of the year has a reference score distinct from the app score. This score reflects the month's intrinsic climate attractiveness, independent of any activity profile.

Three season levels

Each month is first classified by its overall seasonal character: recommended, intermediate or to avoid. This classification considers local reality beyond the numbers alone — for example, a tropical destination during monsoon season is different from a Nordic destination in winter.

Multi-criteria calibration

Within each level, months are differentiated by a combined analysis of thermal comfort, precipitation and sunshine, calibrated on 10 years of ERA5 data. The weighting of these criteria and the comfort functions used come from internal calibration.

Local climate specifics

Some climate regimes require adapted treatment. Destinations with strong monsoon seasonality (Southeast Asia, East Africa) are subject to adjustment: the wet season, although statistically unfavourable, often remains suitable for travel thanks to short, intense rainfall. This context is built into the seasonal classification for these destinations.

ECMWF seasonal correction

When the time horizon falls between D+8 and D+210, the historical climatology is adjusted by anomaly signals from the ECMWF seasonal model. This mechanism progressively merges historical data with projected trends, treating temperature, precipitation and wind differently according to their respective predictability at medium range.

The goal is to detect significant deviations from the norm — an abnormally wet season, a milder winter than average — without claiming the precision of a short-term forecast.

Indicative model accuracy

What our tool does not do

We prefer to be clear about limitations rather than downplay them.

• No microclimates. ERA5's spatial resolution is 31 km — local variations (valleys, coasts, altitude) are not captured. A seaside resort can differ by 5°C from the neighbouring town.
• No climate change modelling. Our 10-year window partially captures recent warming, but doesn't extrapolate future trends. Projections beyond 2030 are outside our scope.
• No ECMWF seasonal correction beyond 7 months. Beyond D+210, the ECMWF seasonal model no longer provides significant predictive value. We then display a pure historical climate profile based on 10 years of data — still useful for long-term planning, but without seasonal trend adjustment.
• Extreme events are not predictable. Cyclones, exceptional storms, historic heatwaves — these rare events are not represented in average statistics.
• No hourly data beyond D+7. For future dates, we display an indicative daily hourly profile, not hour-by-hour forecasts.

Frequently asked questions