Demands are growing rapidly in the operational prediction and applications communities for forecasts that fill the gap between medium-range weather and long-range or seasonal forecasts. Based on the potential for improved forecast skill at the subseasonal to seasonal time range, the Subseasonal to Seasonal (S2S) Prediction research project has been established by the World Weather Research Programme/World Climate Research Programme. A main deliverable of this project is the establishment of an extensive database containing subseasonal (up to 60 days) forecasts, 3 weeks behind real time, and reforecasts from 11 operational centers, modeled in part on the The Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE) database for medium-range forecasts (up to 15 days). The S2S database, available to the research community since May 2015, represents an important tool to advance our understanding of the subseasonal to seasonal time range that has been considered for a long time as a “desert of predictability.” In particular, this database will help identify common successes and shortcomings in the model simulation and prediction of sources of subseasonal to seasonal predictability. For instance, a preliminary study suggests that the S2S models significantly underestimate the amplitude of the Madden–Julian oscillation (MJO) teleconnections over the Euro-Atlantic sector. The S2S database also represents an important tool for case studies of extreme events. For instance, a multimodel combination of S2S models displays higher probability of a landfall over the islands of Vanuatu 2–3 weeks before Tropical Cyclone Pam devastated the islands in March 2015.

Sudden stratospheric warmings (SSWs) are significant source of enhanced subseasonal predictability, but whether this source is untapped in operational models remains an open question. Here we report on the prediction of the SSW on 12 February 2018, its dynamical precursors, and surface climate impacts by an ensemble of dynamical forecast models. The ensemble forecast from 1 February predicted 3 times increased odds of an SSW compared to climatology, although the lead time for SSW prediction varied among individual models. Errors in the forecast location of a Ural high and underestimated magnitude of upward wave activity flux reduced SSW forecast skill. Although the SSW's downward influence was not well forecasted, the observed northern Eurasia cold anomaly following SSW was predicted, albeit with a weaker magnitude, due to persistent tropospheric anomalies. The ensemble forecast from 8 February predicted the SSW, its subsequent downward influence, and a long‐lasting cold anomaly at the surface.

In an attempt to advance the understanding of the Earth's weather and climate by representing deep convection explicitly, we present a global, four‐month simulation (November 2018 to February 2019) with ECMWF's hydrostatic Integrated Forecasting System (IFS) at an average grid spacing of 1.4 km. The impact of explicitly simulating deep convection on the atmospheric circulation and its variability is assessed by comparing the 1.4 km simulation to the equivalent well‐tested and calibrated global simulations at 9 km grid spacing with and without parametrized deep convection. The explicit simulation of deep convection at 1.4 km results in a realistic large‐scale circulation, better representation of convective storm activity, and stronger convective gravity wave activity when compared to the 9 km simulation with parametrized deep convection. Comparison of the 1.4 km simulation to the 9 km simulation without parametrized deep convection shows that switching off deep convection parametrization at a too coarse resolution (i.e., 9 km) generates too strong convective gravity waves. Based on the limited statistics available, improvements to the Madden‐Julian Oscillation or tropical precipitation are not observed at 1.4 km, suggesting that other Earth system model components and/or their interaction are important for an accurate representation of these processes and may well need adjusting at deep convection resolving resolutions. Overall, the good agreement of the 1.4 km simulation with the 9 km simulation with parametrized deep convection is remarkable, despite one of the most fundamental parametrizations being turned off at 1.4 km resolution and despite no adjustments being made to the remaining parametrizations.

The teleconnection from the Madden‐Julian Oscillation (MJO) provides a source of subseasonal variability and predictability to the North Atlantic‐European (NAE) region. The El Niño‐Southern Oscillation (ENSO) modulates the seasonal mean state, through which the MJO and its teleconnection pattern propagates; however, its impact on this teleconnection to the NAE region has not been investigated. Here we find a robust dependence of the teleconnections from the MJO to NAE weather regimes on the phase of ENSO. We show that the MJO to NAO+ regime tropospheric teleconnection is strongly enhanced during El Niño years, via enhanced Rossby wave activity, and suppressed during La Niña. Conversely, the MJO to NAO− regime stratospheric teleconnection is enhanced during La Niña years and suppressed during El Niño. This dependence on the background state has strong implications for subseasonal predictability, including interannual variations in subseasonal predictive skill.

Record‐breaking above‐normal temperatures were observed across western North America in June–July 2021. In this study, our ability to predict this heatwave 2–3 weeks in advance is assessed based on 10 Subseasonal‐to‐Seasonal prediction models. It is found that the above normal temperature in Western Canada during June 28–July 4 was predicted by most models as early as June 10. However, for the forecasts initialized earlier than June 17, not a single ensemble member of all the models is able to capture the magnitude of the observed temperature anomaly. We identify two important processes: an upper tropospheric wave train associated with the boreal summer intraseasonal oscillation in Southeast Asia and an anomalous North Pacific atmospheric river leading to high moisture conditions. Most models are able to predict the wave train across the North Pacific. A realistic representation of moisture transport and its pattern appears crucial for the extended‐range forecast of this heatwave.