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Climate change now detectable from any single day of weather at global scale


For generations, climate scientists have educated the public that ‘weather is not climate’, and climate change has been framed as the change in the distribution of weather that slowly emerges from large variability over decades1,2,3,4,5,6,7. However, weather when considered globally is now in uncharted territory. Here we show that on the basis of a single day of globally observed temperature and moisture, we detect the fingerprint of externally driven climate change, and conclude that Earth as a whole is warming. Our detection approach invokes statistical learning and climate model simulations to encapsulate the relationship between spatial patterns of daily temperature and humidity, and key climate change metrics such as annual global mean temperature or Earth’s energy imbalance. Observations are projected onto this relationship to detect climate change. The fingerprint of climate change is detected from any single day in the observed global record since early 2012, and since 1999 on the basis of a year of data. Detection is robust even when ignoring the long-term global warming trend. This complements traditional climate change detection, but also opens broader perspectives for the communication of regional weather events, modifying the climate change narrative: while changes in weather locally are emerging over decades, global climate change is now detected instantaneously.

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Fig. 1: Warming of daily temperatures experienced at the local and global scale.
Fig. 2: The fingerprint of external forcing and its seasonal variation.
Fig. 3: AGMT estimates from models, reanalyses and observations.
Fig. 4: Emergence of externally forced climate change in ‘global weather’ from the noise of natural variability.

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Data availability

All original CMIP5 data, reanalyses and observations used in this study are publicly available under the following URLs. CMIP5 model data:; reanalysis: ERA-Interim (, NCEP/NCAR Reanalysis 1 (, NCEP/NCAR Reanalysis 2 (, Twentieth Century Reanalysis (; observations (monthly): GISTEMP temperature dataset, version 3 (, Cowtan and Way (2014) temperature dataset, version 2 (, Berkeley Earth Monthly Land+Ocean temperature dataset (, Met Office gridded land surface humidity dataset (HadISDH), version (; observations (daily): Berkeley Earth Daily Land temperature dataset (Experimental,, NOAA Optimum Interpolation Sea Surface Temperature (OISST), AVHRR-Only ( All intermediate and derived data from these products (extracted CMIP5 fingerprints and daily/monthly time series of the test statistic (that is, obtained by projecting CMIP5 models, reanalyses and observations individually onto the fingerprints)) are available at

Code availability

All computer code to reproduce the main results and all figures and Extended Data figures is available at


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We thank A. Merrifield, I. Medhaug, G. Obozinski and H. Lange for comments, and we thank U. Beyerle and J. Sedlàček for the preparation and maintenance of CMIP5 data. We acknowledge funding received from the Swiss Data Science Centre within the project ‘Data Science-informed attribution of changes in the Hydrological cycle’ (DASH, ID C17-01). We thank the observers, creators, maintainers and providers of all datasets. Support for the Twentieth Century Reanalysis Project version 2c dataset is provided by the US Department of Energy, Office of Science Biological and Environmental Research (BER), and by the National Oceanic and Atmospheric Administration Climate Program Office. NCEP Reanalysis and NCEP Reanalysis 2 data were provided by the NOAA/OAR/ESRL PSD, Boulder, CO, USA. We thank the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modelling groups for producing and making available their model output. For CMIP, the US Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led the development of software infrastructure in partnership with the Global Organization for Earth System Science Portals.

Author information

Authors and Affiliations



S.S. conceived the study with N.M. and R.K. and conducted the statistical analysis. All authors contributed to the interpretation of the results and the writing of the manuscript.

Corresponding author

Correspondence to Sebastian Sippel.

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The authors declare no competing interests.

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Peer review information Nature Climate Change thanks Karen McKinnon and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Illustration of daily climate change detection method based on statistical learning.

Statistical detection of externally forced climate change involves three steps. (1) A fingerprint \(\hat{\mathbf{\upgamma}}\) of external forcing on climate is extracted, using regularized linear regression, that relates model simulated spatial patterns of daily temperatures (\({X}_{\rm{mod}}\)) and a defined univariate target variable used as test statistic (denoted \({\mathbf{Y}}_{\rm{mod}}\) in the figure: for example annual global mean temperature, AGMT, or decadal-scale Earth’s Energy Imbalance). (2) Spatial patterns of daily temperatures (\({X}_{\rm{obs}}\)) are projected onto the fingerprint \(\hat{\mathbf{\upgamma}}\) to predict the target variable (denoted \({\hat{\mathbf{Y}}}_{\rm{obs}}\)). (3) A statistical significance test is used to infer whether external forcing on the climate system can be detected from the observed daily temperature pattern against the distribution of the test statistic under natural variability (denoted here \({\mathcal{P}}[{\hat{\mathbf{Y}}}_{\rm{mod*}}]\)).

Extended Data Fig. 2 Evaluation of Annual Global Mean Temperature prediction performance.

Evaluation of prediction performance for the Annual Global Mean Temperature (AGMT) target metric across the multi-model CMIP5 archive from (a) any individual day’s global temperature pattern (‘Mean Included’) and (b) the daily pattern of combined temperature and land humidity with the mean removed (‘Mean Removed’).

Extended Data Fig. 3 Time scale dependence of climate change detection.

(a) Dependence of overall minimum of the test statistic on the time scale of aggregation, shown over different time periods (coloured dots and lines) for the reanalysis time series. Dashed lines show statistical fits of a linear model in log-log space to each period and its extrapolation to sub-daily time scales. The different time scales of aggregation are obtained by successively aggregating the daily test statistics to longer time scales. The CMIP5 1870-1950 distribution of the daily test statistic is shown for comparison. (b) The year of emergence (that is ‘detection’ at any time) of global climate as a function of time scale. The figure is derived by finding, for each time scale and backwards in time from 2018, the first year in which any point does not exceed the 97.5th percentile of the CMIP5 1870-1950 reference distribution of the daily test statistic from (a). Over the last 20 years, climate change would have been detectable in any individual 365-day period, whereas over the last 10 years any 180-day period was detectable in reanalyses and observations. Over the last seven years, detection was possible for any individual day, and would have likely been possible even for shorter time periods. Detection in the experimental daily observational dataset (OISST+BEST) occurs slightly earlier than in daily reanalyses.

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Sippel, S., Meinshausen, N., Fischer, E.M. et al. Climate change now detectable from any single day of weather at global scale. Nat. Clim. Chang. 10, 35–41 (2020).

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