Gabriel Moldovan, Ewan Pinnington, Ana Prieto Nemesio, Simon Lang, Zied Ben Bouallègue, Jesper Dramsch, Mihai Alexe, Mario Santa Cruz, Sara Hahner, Harrison Cook, Helen Theissen, Mariana Clare, Cathal O'Brien, Jan Polster, Linus Magnusson, Gert Mertes, Florian Pinault, Baudouin Raoult, Patricia de Rosnay, Richard Forbes, Matthew Chantry
Abstract. We present version 1.1.0 of ECMWF's Artificial Intelligence Forecasting System (AIFS Single), operational since 25 February 2025. The revised system introduces a bounding-layer framework that enforces physical constraints, such as non-negativity and internal consistency within precipitation and cloud cover variables, alongside expanded training data, revised loss weighting, and an extended set of surface and atmospheric variables. Overall skill improves by 4 %–6 % in the upper air and near-surface variables without degradation of spatial variability. A controlled comparison shows that training data expansion is the dominant source of upper-air skill gains, highlighting the importance of frequent model updates. The bounding framework delivers the largest precipitation improvements, up to 12 % and an approximately 1 d advantage using a categorical measure of skill. We further show that enforcing precipitation non-negativity resolves a gradient ambiguity at the zero-precipitation boundary under MSE training, explaining the reduction in drizzle bias and the improvements in precipitation.