Identifying the influence of climate change on extreme events
Climate Signals illustrates factors that link individual events to broader climate change trends, projections, or physical processes. Climate Signals does not quantify the relative strength of these factors, with one important exception: in the event that scientists have done an attribution study.
Climate attribution looks at the influence of climate change on extreme events and is a leading frontier of today’s climate science. While natural variation is the dominant determinant for the frequency and severity of most climate events, climate change is now an additional factor to consider when examining the context and shape of any climate extreme. Scientists have firmly established the fingerprint of global warming for many trends.
The question is not "did climate change cause this event?"
Climate change does not cause extreme climate events. At a more fundamental level, global warming has changed the background conditions in which all climate-related events occur. In this sense, climate change is now affecting all extreme climate events and, by pushing systems beyond their thresholds, can be responsible for a disproportionate amount of an event's impacts. 
To understand the influence of global warming on specific events, scientists might ask: 
- Has the likelihood or strength of a given event changed in the observational record?
- Are changes in the observational record consistent with climate change models?
Or they might attempt to characterize the conditions in which the event occurred, by asking:
- What was the role of internal natural variability in setting up the observed pattern?
When an extreme climate event is determined to have “no link” to climate change, this does not necessarily mean there is no connection—it just means that a particular research team did not find evidence supporting a conclusive causative relationship.
Attribution study types
There are a variety of attribution studies, and these are often mixed up or put in competition with one another, so recognizing their distinctions and how they often complement each other is of critical importance to understanding our global, and complex, climate system.
The term temperature anomaly means a departure from a reference value or long-term average. A positive anomaly indicates that the observed temperature was warmer than the reference value, while a negative anomaly indicates that the observed temperature was cooler than the reference value. Some studies investigate the likely impact of global warming in recent decades by analyzing temperature anomalies.
Probabilistic event attribution using model comparisons
These types of studies compare the probability of an event happening in the real world with the probability of the event happening in a modeled world without global warming. Some weather events are easier for scientists to work with than others. There is generally more confidence in studies focusing on heatwaves than those focusing on extreme precipitation or hurricanes, which are currently difficult for models to simulate. Similarly, models have an easier time in certain regions. The mid-latitudes, for example, are easier to model because the weather is more random and so it is not necessary to get large-scale climate trends exactly right. The limited number of events studied so far makes for a patchy picture of how climate change impacts extreme weather events, and scientists are currently working to fill in the gaps. An exciting new development is the new World Weather Attribution Project, which will attempt to perform real-time model comparisons to the “world that might have been.”
These studies compare observed extreme events with paleoclimate reconstructions from evidence. This only works for some types of extreme weather events, as many others do not show up in the paleoclimate record. Some examples include drought, wildfire, and temperature records. One advantage of paleoclimate studies is they can speak to whether the rate of occurrence of a kind of event has increased, as well as the comparable historic intensity of an event. These studies do, however, require additional data to draw a direct connection to climate.
Observational climate attribution studies compare a baseline within the instrumental record (which likely already incorporates some warming) with a more recent period such as the past 30 years. One example is James Hansen’s study of heat waves by surface area, which uses the modern observational record to identify recent extreme temperature anomalies.  Studies correlating sea level rise and flooding events also often include an observational component, given the large amount of observational data on sea level rise.