Abstract
Skilful seasonal climate forecasts have potential to affect decision making in agriculture, health and water management. Organizations
such as the National Oceanic and Atmospheric Administration (NOAA) are currently planning to move towards a climate services
paradigm, which will rest heavily on skilful forecasts at seasonal (1 to 9 months) timescales from coupled atmosphere-land-ocean
models. We present a careful analysis of the predictive skill of temperature and precipitation from eight seasonal climate
forecast models with the joint distribution of observations and forecasts. Using the correlation coefficient, a shift in the
conditional distribution of the observations given a forecast can be detected, which determines the usefulness of the forecast
for applications. Results suggest there is a deficiency of skill in the forecasts beyond month-1, with precipitation having
a more pronounced drop in skill than temperature. At long lead times only the equatorial Pacific Ocean exhibits significant
skill. This could have an influence on the planned use of seasonal forecasts in climate services and these results may also
be seen as a benchmark of current climate prediction capability using (dynamic) couple models.
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