Droughts are more and more often a limiting factor in agricultural production and can have severe negative effects on food security in vulnerable countries. Global agriculture early warning systems monitor agriculture in near real-time by analyzing meteorological data (e.g., precipitation and temperature) and optical remote sensing data as proxy vegetation health to detect possible negative anomalies and trigger warnings. Seasonal climate forecasting can add a predictive component and inform about upcoming precipitation deficits, thus allowing anticipation and improved planning of response actions. Here, we propose a scheme to adapt the standard precipitation forecast from the seasonal Copernicus Climate Change Service multi-system to crop and rangeland phenology, making it suitable for agricultural early warning.

We start by making tercile maps that show the likelihood of each tercile (drier than normal, normal, wetter than normal) for each of the six-month forecasting periods.

These maps show the skills for each possible monthly period combination.

Afterward, agronomically relevant tercile maps are produced for the closest season in time at any location. Mosaicing the forecasts for the appropriate growing season period in each grid cell yields these maps.

The resulting map shows the tercile probability for the remaining part of the ongoing growing season (if any at the time of analysis) or the probability of the next upcoming season (if in between growing seasons at the time of analysis).

The proposed methodology offers a precipitation seasonal forecast product ready to use by agricultural analysts and directly ingestible by automatic warning systems.