Abstract Accurate subseasonal-to-seasonal (S2S) weather forecasts are crucial to making important decisions in many sectors. However, significant gaps exist between the needs of society and what forecasters can produce, especially at weekly longer lead times. We hypothesize that by clustering atmospheric states into a number predefined categories, noise be reduced and, consequently, medium-rang...
