Exploiting the signal to noise ratio in multi-system predictions of summertime precipitation and maximum temperatures in Europe

Abstract. Droughts and heatwaves are among the most impactful climate extremes. Their co-occurrence can have devastating consequences on natural and human systems. Early information on seasonal timescales on their possible occurrence is beneficial for many stakeholders. Seasonal climate forecast has gradually become more used; but limited skill in certain regions and seasons still hinders a broader use. Here we show that a simple forecast metric from a multi-system ensemble, the signal to noise ratio, can help overcome some limitations in the boreal summer. Forecasts of maximum daily near surface air temperature and precipitation in boreal summers with high signal to noise ratio tend to coincide with observed larger deviations from the mean than years with small signal to noise ratio. The same metric also helps identify processes relevant to seasonal climate predictability. Here we show that a positive phase of a North Atlantic Sea surface dipole during boreal spring may favor the occurrence of dry and hot summers in Europe.