We are a group of climate scientists, hydrologists, environmental engineers and data scientists who work on better understanding hydroclimate and its variability across scales, better quantifying and attributing predictability of the Earth system, and exploring adaptation strategies in the face of climate change. In doing so, we use, develop and assess state-of-the-art data science techniques, such deep learning models and expainable artificial intelligence (AI).

Past and current areas of research include improving predictive skill of hydroclimate and extreme events, understanding climate teleconnections and predictability, advancing climate attribution and causal discovery, climate downscaling, among others.

Some representative papers from our Lab have garnered international attention and have been highlighted by publishers. Examples include "A new interhemispheric teleconnection increases predictability of winter precipitation in southwestern US," published in Nature Communications; "Zonally contrasting shifts of the tropical rain belt in response to climate change," published in Nature Climate Change; "Underestimated MJO variability in CMIP6 models," published in Geophysical Research Letters; and "Climate-driven changes in the predictability of seasonal precipitation" published in Nature Communications.

​

Read more about our research here.