Introducing long-term trends into subseasonal temperature forecasts through trend-aware postprocessing

Skilful subseasonal forecasts are crucial for issuing early warnings of extreme weather events, such as heatwaves and floods. Operational subseasonal climate forecasts are often produced by global climate models not dissimilar to seasonal forecast models, which typically fail to reproduce observed temperature trends. In this study, we identify that the same issue exists in the subseasonal forecasting system. Subsequently, we adapt a trend-aware forecast postprocessing method, previously developed for seasonal forecasts, to calibrate and correct the trend in subseasonal forecasts. We modify the method to embed 30-year climate trends into the calibrated forecasts even when the available hindcast period is shorter. The use of 30-year trends is to robustly represent long-term climate changes and overcome the problem that trends inferred from a shorter period may be subject to large sampling variability. Calibration is applied to 20-year ECMWF subseasonal forecasts and AWAP observations of Australian minimum and maximum temperatures with forecast horizons of up to 4 weeks. Relative to day-of-year climatology, raw week-1 forecasts reproduce temperature trends of the 20-year observations in many regions while raw week-4 forecasts do not exhibit the 20-year observed trends. After trend-aware postprocessing, the behaviour of forecast trends is related to raw forecast skill regarding accuracy. Calibrated week-1 forecasts show apparent trends consistent with the 20-year observations, as the calibration transfers forecast skill and embeds the 20-year observed trends into the forecasts when raw forecasts are inherently skilful. In contrast, calibrated week-4 forecasts exhibit the 30-year observed trends, as the calibration reverts the forecasts to the 30-year observed climatology with trends when raw forecasts have little skill. For both weeks, the trend-aware calibrated forecasts are more reliable, and as skilful as or more skilful than raw forecasts. The extended trend-aware method can be applied to deliver high-quality subseasonal forecasts and support decision-making in a changing climate.