Supplementary data for storyline approaches to assess low-likelihood high-impact outcomes under different emission scenarios

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Authors: Clemens Schwingshackl, Erich Markus Fischer, Jana Sillmann
Cited by: 0

Abstract

This dataset contains the supplementary data for a manuscript on storyline approaches to assess low-likelihood high-impact outcomes under different emission scenarios. The dataset contains estimates of 7-day running mean of daily maximum temperatures (Tx7day), precipitation minus evapotranspiration (P-E), and annual maximum daily maximum precipitation (Rx1day) for the scenarios SSP1-2.6, SSP2-4.5, and SSP3-7.0 for multiple CMIP6 models. Data are available averaged over two different time periods (1995-2014, 2081-2100) and for different storyline approaches (nearest neighbour, warming level, pattern scaling). Additionally, time series of global mean surface air temperature (GSAT) are available. Ancillary data contain the SREX regions at 1° spatial resolution as well as the five nearest-neighbour models for each SSP-RCP scenario. Note that the nearest-neighbour models available here are calculated based on the full model ensemble, that is without considering potential model interdependencies.

Fields of Study & Subjects

Climate variability and models Climate Change and Environmental Impact Climate Change Policy and Economics

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
30 January 2026