The widespread impacts of declining Arctic sea ice necessitate accurate and reliable predictions. While much focus has been placed on sub-seasonal to seasonal forecasts or multi-decadal projections, seasonal to interannual predictions – crucial for planning and infrastructure – have received less emphasis. Internal climate variability is a dominant source of uncertainty on these timescales, yet initialized dynamical climate model predictions have limited usefulness due to biases and long-term drift that leads to poor skill beyond seasonal timescales. This study develops statistical models – transfer operators (TO) and neural networks (NN) – to forecast probabilistic state transitions of Arctic September sea ice extent (SIE) internal variability. Trained on 24,420 transitions from the CMIP6 archive, these models make accurate and reliable predictions across multiple initialization months. At interannual timescales, they outperform simple persistence in predicting SIE trends. At seasonal timescales, their skill is comparable to other numerical and statistical models in the Sea Ice Outlook. While TO performance declines for spring initializations, NNs incorporating information about the area of thick ice can overcome the spring predictability barrier for March–May initializations. For the next decade, the TO suggests that September SIE will likely remain above the projected forced trend, while the NN predicts it will likely be lower. However, both models predict that the trend in September SIE will likely be higher (65–95% chance) than the CMIP6 projected forced trend over the next three years, suggesting near-term stability. These results highlight the potential of statistical approaches for improving Arctic sea ice predictions on critical planning timescales.