Source code to produce the data, analysis and figures for the paper Reconstruction of hourly coastal water levels and counterfactuals without sea level rise for impact attribution which is published in Earth System Science Data (ESSD) Abstract. Rising seas are a threat for human and natural systems along coastlines. The relation between global warming and sea-level rise is established, but the quantification of impacts of historical sea-level rise on a global scale is largely absent. To foster such quantification, we here present a reconstruction of historical hourly (1979-2015) and monthly (1900-2015) coastal water levels and a corresponding counterfactual without long-term trends in sea level. The dataset pair allows for impact attribution studies that quantify the contribution of sea level rise to observed changes in coastal systems following the definition of the Intergovernmental Panel on Climate Change (IPCC). Impacts are ultimately caused by water levels that are relative to the local land height, which makes the inclusion of vertical land motion a necessary step. Also, many impacts are driven by sub-daily extreme water levels. To capture these aspects, the factual data combines reconstructed geocentric sea level on a monthly time scale since 1900, vertical land motion since 1900 and hourly storm-tide variations since 1979. The inclusion of observation-based vertical land motion brings the trends of the combined dataset closer to tide gauge records in most cases, but outliers remain. Daily maximum water levels get in closer agreement with tide gauges through the inclusion of intra-annual ocean density variations. The counterfactual data is derived from the factual data through subtraction of the quadratic trend. The dataset is made available openly through the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP).

Acknowledgments This research has received funding from the German Federal Ministry Ministry of Education and Research (BMBF) under the research projects QUIDIC (01LP1907A) and ISIAccess (16QK05), the European Union's Horizon 2020 research and innovation programme under agreement No 820712grant, and is based upon work from COST Action CA19139 PROCLIAS (PROcess-based models for CLimate Impact Attribution across Sectors), supported by COST (European Cooperation in Science and Technology; https://www.cost.eu). T.W. acknowledges support by NASA's Sea Level Change Team (award number 80NSSC20K1241) and the National Science Foundation (award numbers 1854896 and 2141461). S.D. acknowledges support by NASA's Sea Level Change Team (award number 80NSSC20K1241) and David and Jane Flowerree for their endowment.