… the marriage of machine-learning techniques with climate science. In machine learning, AI
… This approach is a natural fit for climate science: a single run of a high-resolution climate …

… To explore the applicability of machine learning methods to streamflow forecasting, we
chose Stave River above Stave Lake in southern British Columbia, Canada (Fig. 1), with a …

… machine learning should reflect this. While the use of machine learning to emulate
weather forecast models is a relatively new endeavour, there is a rich history of climate model …

… as in climate analyses. We show examples of the use of machine learning techniques as a
… and complex issues in weather forecasting and in the study of climate change over different …

… 10 Machine Learning (ML) approaches to sub-seasonal temperature forecasting over the
contiguous US on the SSF dataset we collect, including a variety of climate variables from the …

… climate forecast data was not significant under the conditions used in this study, but further
improvement is expected if forecast … Drought forecasting was performed by machine learning …

… machine learning models selected in this study will achieve the best performance for forecasting
… ensemble improve the inflow forecast performance as compared to individual machine …

… models we use to forecast weather and climate with machine learning. In particular, our …
of precipitation forecasts with these ML moist physics parametrizations. Such forecasts, with …

Global warming is one of the most complicated challenges of our time causing considerable
tension on our societies and on the environment. The impacts of global warming are felt …

… (S2S) climate forecasting, using machine learning techniques to model relationships between
gridded global climate model (GCM) outputs and observed climate … climate forecasts. In …