Comment on "When are AI models ready for deployment? reassessing Google's global AI flood forecasting system through the lens of responsible modelling"

This preprint is a formal technical comment on the recent critique by Li et al. (2026) published in Journal of Hydrology X, which evaluated the global AI flood forecasting system introduced in Nearing et al. (2024, Nature). Recently, Li et al. (2026) evaluated our global AI flood forecasting system. This evaluation highlights a growing distinction in modern hydrology between physical process modeling vs. models that support humanitarian action. By examining the framing of operational readiness, thresholding, extreme event definitions, event windows, and benchmarking through a humanitarian lens, we demonstrate that the evaluation metrics chosen by Nearing et al. (2024) align with established disaster response protocols and end-user needs. Answering Li et al.’s call for rigor and transparency, the first principle of model evaluation is to assess a model for its intended purpose.