ERA5-ecPoint: what it is and its power in the contextualisation of severe temperatures and rainfall

The continuous breaking of temperature and rainfall records worldwide is a stark reminder that climate is changing. These record-breaking events underscore the urgent need to understand and adapt to these changes as they can lead to more frequent and severe weather events, e.g. heatwaves (or cold waves), extreme (localised) rainfall, and (flash) floods. Hence, investing in sustainable practices, enhancing forecasting and response strategies, and implementing policies to mitigate climate change effects is critical. But to do so, we need appropriate data to inform our decisions.Observation-based climatologies help detect trends and patterns in the climate over a long period of time and can contextualise extreme, high-impact weather events. However, observations can be inaccurate and unevenly distributed in space and time. Reanalysis such as ERA5 provides a good alternative to observation-based climatologies because it provides accurate, temporally consistent, gridded estimates of the past state of the Earth system worldwide. However, this only satisfies some needs due to ERA5's relatively coarse model resolution (precluding the representation of localised extremes) and some intrinsic biases.Within the Highlander project, co‑financed by the EU and coordinated by Italy's Cineca computing centre, ECMWF's ecPoint post-processing technique was applied to raw ERA5 'deterministic' fields to address ERA5 limitations and create much more reliable (probabilistic) point-scale climatologies. With ERA5-ecPoint to be released in the Copernicus Climate Data Store later this year, this presentation is addressed to future users of ERA5-ecPoint. The main goal of the presentation is to provide examples of how this dataset can help understand and contextualise the occurrence of low-probability but high-impact weather, such as extremely low or high temperatures and extreme (localised) rainfall, and how to do so in a changing climate.