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Seasonal climate prediction to date has largely focussed on probabilistic forecasts for above‐ and below‐average conditions in climate means. Here, we examine the possibility of making sub‐seasonal to seasonal outlooks for daily‐scale precipitation extremes in Australia. We first use observational data to show that significant relationships exist between climate modes, such as the El Niño–Southern Oscillation, and indices representing rainfall extremes across much of Australia. The strong observed teleconnections between climate modes and daily rainfall extremes suggest the potential for predictability on seasonal scales. The current Australian Bureau of Meteorology seasonal prediction system (ACCESS‐S1) is examined for performance in predicting rainfall extreme indices using a range of measures. Ensemble hindcasts, consisting of 11 members initialised every month during 1990–2012, perform well for some extreme rainfall indices on short lead‐times (up to 1 month). We note that at short lead‐times, forecasts are aided by skilful weather prediction, so forecast performance drops at lead‐times of a week or more. Forecast performance is lower in austral summer than other seasons and greater in the north and interior of the continent, particularly in the dry season, than elsewhere. The ACCESS‐S1 ensemble is overconfident but exhibits some reliability in probabilistic forecasts of above‐ or below‐average number of wet days and intensity of the highest daily maximum precipitation, especially in northern Australia. ACCESS‐S1 captures the broad pattern of relationships between climate modes and rainfall extremes that are observed. For two case‐studies of unusually extreme precipitation, ACCESS‐S exhibits contrasting performance for forecasts of extreme rainfall anomalies beyond the first month. These results suggest that ACCESS‐S1 may be used to produce outlooks for some rainfall indices, such as the number of wet days and the intensity of the wettest day, for the month ahead. [ABSTRACT FROM AUTHOR]

Extended warning of above‐average and extreme precipitation is valuable to a wide range of stakeholders. However, the sporadic nature of precipitation makes it difficult to forecast skilfully beyond one week. Subseasonal forecasting is a growing area of science that aims to predict average weather conditions multiple weeks in advance using dynamical models. Building on recent work in this area, we test the hypothesis that using large‐scale horizontal moisture transport as a predictor for precipitation may increase the forecast skill of the above‐median and high‐precipitation weeks on subseasonal time‐scales. We analysed retrospective forecast (hindcast) sets from the Australian Bureau of Meteorology's latest operational subseasonal‐to‐seasonal forecasting model, ACCESS‐S2, to compare the forecast skill of precipitation using integrated water vapour transport (IVT) as a proxy, compared to using precipitation forecasts directly. We show that ACCESS‐S2 precipitation generally produces more skilful forecasts, except over some regions where IVT could be a useful additional diagnostic for warning of heavy precipitation events. [ABSTRACT FROM AUTHOR]