Anthropogenic climate change (ACC) is critically influencing numerous extreme events worldwide, leading to the development of rapid attribution frameworks that allow for the timely evaluation of the role of ACC in changes in the frequency and intensity of specific extreme events. ClimaMeter (Faranda et al., 2024) is one of the tools recently developed to contextualise extreme weather events relative to climate change. ClimaMeter analyses extreme events shortly after they occur and leverages the analogue methodology for conditional attribution to evaluate whether and how events similar to the one analysed have changed in the recent climate. In order to attribute such changes to ACC, natural variability and its contribution must be quantified. In ClimaMeter, three modes of sea surface temperature variability are considered: the El Niño–Southern Oscillation, the Atlantic Multidecadal Oscillation, and the Pacific Decadal Oscillation. These three modes are considered with equal weight regardless of the event’s location and type. Moreover, ACC is implicitly considered the primary factor influencing the occurrence of the event; therefore, changes not explained by natural variability modes are assumed to be attributable to ACC. Such an approach has potential limitations, which we address in this paper by proposing a refined and more flexible version, called ClimaMeter 2.0. First, we propose weighting the three modes of variability according to the strength of the teleconnection between the remote modes and the local hazards. Then, we test the hypothesis that ACC has critically influenced the observed changes by analysing long-term trends in specific quantiles of the local hazard variables. After extensive testing using pre-industrial climate simulations and observational data, we conclude that, while remaining within the same conceptual framework, ClimaMeter 2.0 provides greater flexibility and enables a more nuanced assessment of the influence of ACC on specific extreme events.