Abstract
The role of climate change in increasing the probability of single extreme events is now well established. However, climate change also influences compound and consecutive extremes, whose impacts can be amplified when events occur close in time or simultaneously. Despite their relevance, these events are still largely overlooked in attribution studies.
The C-IMPACT project aims to introduce a simple and flexible methodology to attribute compound and consecutive extremes using binary representations of hazards. Our results show that considering temporal dependencies between extremes can increase probability ratios and reveal impacts that may otherwise be underestimated.
This work highlights the importance of moving toward multivariate attribution approaches to better support climate adaptation and policy decisions in a warming world.
About Cristina
Cristina Deidda comes from Cagliari, in Sardinia, she earned her PhD in Environmental and Infrastructure Engineering from Politecnico di Milano, where she focused on multivariate statistical analysis of extreme events. Her research explored intercorrelated extremes and led to the development of new methodologies to study asymmetry, dependence, and causal relationships among variables.
In 2024, she was awarded a Marie Skłodowska-Curie Fellowship for the C-IMPACT project (Compound Impacts in MultiPle Sectors Attributed to Climate Trends) and joined a climate research group at VUB in Brussels. Her current research investigates how climate change affects the likelihood of compound extreme events, such as consecutive hot and dry events. She has collaborated with the Statistical Research Center in Geneva and with World Weather Attribution to develop multivariate methods for extreme event attribution.
In parallel, she contributed to policy-oriented projects as a consultant for ECMWF and for the Directorate-General for Mobility and Transport of the European Commission, focusing on climate adaptation, cross-border investments, and the impacts of climate change on transport infrastructure.