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.
Moldovan et al. (2026) studied this question.