We present here ATDT (Attribution Tool for Daily-to-monthly Temperatures), a method for quantifying the effect of climate change on local daily mean, maximum and minimum temperatures as well as their 2–31-day moving averages for locations that have long observational time-series available. The method utilizes CMIP6 (Coupled Model Intercomparison Project Phase 6) model data and a time series of global mean temperature to estimate climate-change-induced shifts in both the mean and variance of local temperature distributions. As a case study, we apply the method to 23 weather stations in Fennoscandia for two 14-day periods in July of 2025, when an intense heatwave occurred in Northern Europe. We find that the station-specific average maximum temperatures in 12–25 July were 1.5 to 2.4 °C higher, whereas the minimum temperatures in 19 July to 1 August were 1.4 to 2.3 °C higher than they would have been under pre-industrial conditions represented by the year 1900. Furthermore, we estimate that these temperatures were made 3.0–14.6 times (maximum temperature) and 6.1–22.9 times (minimum temperature) more likely by climate change since the year 1900. The presented method enables real-time attribution of climate change on observed temperatures and can support operational meteorologists in climate change communication.