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Providing comprehensive regional- and local-scale information on changes observed in the climate system plays a vital role in planning effective and efficient climate change adaptation options, specifically over resource-limited regions. Here, we assess changes in temperature and heat waves over different regions of the African continent, with a focus on spatiotemporal trends and the time of emergence of change in hot extremes from natural variability. We analyse absolute and relative threshold indices. Data sets include temperatures from observations (CRUTS4.03 and BEST) and from three representative state-of-the-art reanalyses (ERA5, MERRA2 and JRA-55) for the common period 1980–2018. Statistically significant warming is observed over all regions of Africa in temperature time series from CRU observations and reanalysis data, although the trend strength varies between data sets. Also, extreme temperatures and heat wave indices from BEST observations and all reanalysis data sets reveal increasing trends over all regions of the African continent. However, there are differences in both trend strength and time evolution of heat wave indices between different reanalysis data sets. Most data sets agree in identifying 2010 as a peak heat year over Northern and Western Africa while Eastern and Southern Africa experienced the highest heat wave occurrence in 2016. Our results clearly reveal that heat wave occurrences have emerged from natural climate variability in Africa. The earliest time of emergence takes place in the Northern Africa region in the early 2000s while in the other African regions emergence over natural variability is found mainly after 2010. This also depends on the respective index metrics, where indices based on more consecutive days show later emergence of heat wave trends. Overall, significant warming and an increase in heat wave occurrence is found in all regions of Africa and has emerged from natural variability in the past one or two decades.

Observations facilitate model evaluation and provide constraints that are relevant to future predictions and projections. Constraints for uninitialized projections are generally based on model performance in simulating climatology and climate change. For initialized predictions, skill scores over the hindcast period provide insight into the relative performance of models, and the value of initialization as compared to projections. Predictions and projections combined can, in principle, provide seamless decadal to multi-decadal climate information. For that, though, the role of observations in skill estimates and constraints needs to be understood in order to use both consistently across the prediction and projection time horizons. This paper discusses the challenges in doing so, illustrated by examples of state-of-the-art methods for predicting and projecting changes in European climate. It discusses constraints across prediction and projection methods, their interpretation, and the metrics that drive them such as process accuracy, accurate trends or high signal-to-noise ratio. We also discuss the potential to combine constraints to arrive at more reliable climate prediction systems from years to decades. To illustrate constraints on projections, we discuss their use in the UK's climate prediction system UKCP18, the case of model performance weights obtained from the Climate model Weighting by Independence and Performance (ClimWIP) method, and the estimated magnitude of the forced signal in observations from detection and attribution. For initialized predictions, skill scores are used to evaluate which models perform well, what might contribute to this performance, and how skill may vary over time. Skill estimates also vary with different phases of climate variability and climatic conditions, and are influenced by the presence of external forcing. This complicates the systematic use of observational constraints. Furthermore, we illustrate that sub-selecting simulations from large ensembles based on reproduction of the observed evolution of climate variations is a good testbed for combining projections and predictions. Finally, the methods described in this paper potentially add value to projections and predictions for users, but must be used with caution. All authors were supported by the EUCP project funded by the European Commission's Horizon 2020 programme, Grant Agreement number 776613. JM was also supported by the french ANR MOPGA project ARCHANGE and by the EU-H2020 Blue Action (GA 727852) and 4C projects (GA 821003). MGD also received funding by the Spanish Ministry for the Economy, Industry and Competitiveness grant reference RYC-2017-22964. Peer Reviewed "Article signat per 14 autors/es: Gabriele C. Hegerl, Andrew P. Ballinger, Ben B. B. Booth, Leonard F. Borchert, Lukas Brunner, Markus G. Donat, Francisco J. Doblas-Reyes, Glen R. Harris, Jason Lowe, Rashed Mahmood, Juliette Mignot, James M. Murphy, Didier Swingedouw and Antje Weisheimer"

Future tropical cyclone activity is a topic of great scientific and societal interest. In the absence of a climate theory of tropical cyclogenesis, general circulation models are the primary tool available for investigating the issue. However, the identification of tropical cyclones in model data at moderate resolution is complex, and numerous schemes have been developed for their detection. The influence of different tracking schemes on detected tropical cyclone activity and responses in the Hurricane Working Group experiments is examined herein. These are idealized atmospheric general circulation model experiments aimed at determining and distinguishing the effects of increased sea surface temperature and other increased CO2 effects on tropical cyclone activity. Two tracking schemes are applied to these data and the tracks provided by each modeling group are analyzed. The results herein indicate moderate agreement between the different tracking methods, with some models and experiments showing better agreement across schemes than others. When comparing responses between experiments, it is found that much of the disagreement between schemes is due to differences in duration, wind speed, and formation-latitude thresholds. After homogenization in these thresholds, agreement between different tracking methods is improved. However, much disagreement remains, accountable for by more fundamental differences between the tracking schemes. The results indicate that sensitivity testing and selection of objective thresholds are the key factors in obtaining meaningful, reproducible results when tracking tropical cyclones in climate model data at these resolutions, but that more fundamental differences between tracking methods can also have a significant impact on the responses in activity detected.

Climate models predict a strengthening contrast between wet and dry regions in the tropics and subtropics (30 °S–30 °N), and data from the latest model intercomparison project (CMIP6) support this expectation. Rainfall in ascending regions increases, and in descending regions decreases in climate models, reanalyses, and observational data. This strengthening contrast can be captured by tracking the rainfall change each month in the wettest and driest third of the tropics and subtropics combined. Since wet and dry regions are selected individually every month for each model ensemble member, and the observations, this analysis is largely unaffected by biases in location of precipitation features. Blended satellite and in situ data from 1988–2019 support the CMIP6-model-simulated tendency of sharpening contrasts between wet and dry regions, with rainfall in wet regions increasing substantially opposed by a slight decrease in dry regions. We detect the effect of external forcings on tropical and subtropical observed precipitation in wet and dry regions combined, and attribute this change for the first time to anthropogenic and natural forcings separately. Our results show that most of the observed change has been caused by increasing greenhouse gases. Natural forcings also contribute, following the drop in wet-region precipitation after the 1991 eruption of Mount Pinatubo, while anthropogenic aerosol effects show only weak trends in tropic-wide wet and dry regions consistent with flat global aerosol forcing over the analysis period. The observed response to external forcing is significantly larger (p > 0.95) than the multi-model mean simulated response. As expected from climate models, the observed signal strengthens further when focusing on the wet tail of spatial distributions in both models and data.