Hidden Tropical Pacific Sea Surface Temperature States Reveal Global Predictability for Monthly Precipitation for Sub‐Season to Annual Scales

We present the first global precipitation predictability estimates corresponding to the recently discovered flavors of El Nino Southern Oscillation (ENSO) that are encoded in the hidden states of Tropical Pacific sea surface temperatures identified using a non-homogeneous hidden Markov model. For each calendar month and for each hidden state, we assess future precipitation predictability through the conditional standardized anomaly of the average and the standard deviation of monthly precipitation, at 1, 3, 6, 9, and 12 months lead times. We find statistically significant potential predictive skill for key regions for each hidden state and calendar month, even for 12-months in the future. We apply the algorithm sequentially over the period of record to identify regions that can be consistently predicted for different lead times and calendar months. The cross-validated correlation skill is demonstrably superior to that of regression with an ENSO index used in the same way.