Significant hindcast skill for the 3-month standardized precipitation index (SPI$_{3M}$) has been so far limited to one lead month. To increase that lead time, we propose to exploit well-known El Ni\~no-Southern Oscillation (ENSO)–precipitation teleconnections through ENSO-state conditioning. We condition initialized seasonal SPI$_{3M}$ hindcasts, derived from the Max-Planck-Institute Earth System Model over the period 1982-2013, on ENSO states by exploring significant agreements between two complementary analyses: hindcast skill ENSO–composites, and observed ENSO–precipitation correlations. Predictions conditioned on autumn (ASO)-ENSO states demonstrate significant and reliable winter (DJF) drought hindcast skill up to lead month 4 in equatorial South- and southern North America. The area of reliable drought hindcast skill is further enlarged when the respective region’s dry ENSO phase is already present in the antecedent summer (JJA-ENSO-state-conditioned). In contrast to previous studies, our evaluation separates predictions and observations. Thereby, ENSO-state conditioning demonstrates genuine hindcast skill up to lead month 4.

Variability of the North Atlantic Oscillation (NAO) drives wintertime temperature anomalies in the Northern Hemisphere. Dynamical seasonal prediction systems can skilfully predict the winter NAO. However, prediction of the NAO-dependent air temperature anomalies remains elusive, partially due to the low variability of predicted NAO. Here, we demonstrate a hidden potential of a multi-model ensemble of operational seasonal prediction systems for predicting wintertime temperature by increasing the variability of predicted NAO. We identify and subsample those ensemble members which are close to NAO index estimated from initial autumn conditions. In our novel multi-model approach, the correlation prediction skill for wintertime Central Europe temperature is improved from 0.25 to 0.66, accompanied by an increased winter NAO prediction skill of 0.9. Thereby, temperature anomalies can be skilfully predicted for the upcoming winter over a large part of the Northern Hemisphere through increased variability and skill of predicted NAO.