Subseasonal predictions, which fall between medium-range forecasts of up to 10 days and seasonal predictions of up to 3 months, have gained increasing scientific interest due to a rise in demand for accurate and reliable outlooks. In recent decades, the prediction skill of dynamical forecast systems has greatly improved via the use of ensemble forecasting techniques and improved initializations, couplings, and physical parameterizations (e.g., Saha et al., 2014;Vitart, 2014). Despite this improvement, it remains challenging to produce skillful predictions for surface variables across the globe at lead times of longer than a few weeks (Xiang et al., 2019). This can be attributed to the rapidly worsening prediction skill of dynamical models as initial errors are compounded over time. Nonetheless, dynamical forecast systems generate reliable predictions for major climate modes, such as the North Atlantic Oscillation (NAO) and the Madden-Julian Oscillation (MJO) beyond a subseasonal time scale (e.g., Lin, 2018;Kim et al., 2018).To overcome the shortcomings of dynamical models, statistical models have been developed by employing slowly varying climate modes as a potential source of prediction skill (Baggett et al., 2018;Black et al., 2017;Mundhenk et al., 2018). Johnson et al. (2014) constructed a statistical model, known as a phase model, based on the probability distributions of predictands (e.g., surface air temperature) contingent upon the phases of chosen tropical climate modes, such as the MJO and El Niño-Southern Oscillation (ENSO). For weeks 3-4, the phase model demonstrated marginally higher skill for temperature predictions over the