In climatology, the question of Detection and Attribution (D&A) is fairly well defined: ("Detection" is the process of demonstrating that climate has changed in some defined statistical sense, without providing a reason for that change and "Attribution" is the process of establishing the most likely causes for the detected change with some defined level of confidence, see the IPCC definition).
The field of statistics has become one of the mathematical foundations in D&A studies because computing uncertainties represent difficult inferential challenges when analyzing climate model outputs.
In this context, we will give a brief overview on the main statistical concepts underpinning the D&A and proposes new methodological approaches to revise return periods for record events in a changing climate. We will show the advantages of our method throughout theoretical results and simulation studies.