moderate role, and climate model uncertainty remains large (A2, right). This suggests that it is much more difficult to predict future climates when looking at one country or one region than globally, regardless of future progress in our understanding of climate change. Natural variability means that the climate signal is more difficult to extract (and – as already mentioned – forecasts of future climate remain out of reach).

8. **There is also large uncertainty from differences between global climate models.** The IPCC (2013) provides results from 42 global climate models. The models agree on the very big picture (more warming in high latitude than in low latitude; more precipitations in high latitudes; less precipitation around the tropics; more precipitation around the equator). However, the differences can be huge in some regions (e.g., half of the models predict an increase in precipitation over India; half of the models predict the opposite; and – as a consequence – the “average model” predicts no change, showing the risk of averaging projections).

9. **When looking locally, we usually do not use global climate models.** Instead, we use downscaling techniques which can be done with statistical tools or with regional climate models (RCM). Statistical methods use statistical relationships, calibrated on historical data, to relate large-scale drivers – which climate models can reproduce – to local phenomena – which climate models cannot reproduce (Elsner and Jagger 2006; Mestre and Hallegatte 2009; Nuissier et al. 2012). Even though our knowledge of the laws of physics helps select potential predictors, this method is not directly based on physical laws. Such statistical methods are computationally efficient and reproduce the current climate well. Statistical models, however, have two main drawbacks: first, they need long series of reliable data; second, even with a sufficiently large data range, it is difficult to know whether a statistical relationship will remain valid in a future climate.

10. **To avoid the problem of validity of historical relationships, one may use physical models, which are based on physical laws.** Physical models, which model and simulate biophysical and atmospheric processes, are of particular interest when investigating extreme patterns and variability changes. Of course, physical models often require calibration and bias correction, so the distinction between physical models and statistical models is sometimes fuzzy. Examples are Regional Climate Models (RCMs); see (Knutson et al. 2010). RCMs are difficult to develop and may only have resolution of about 25 km, but they are less data-dependent. The confidence in RCMs is generally higher because they do not have to assume that the relationship between large-scale and small-scale climate variables remains constant in a future – possibly very different – climate. Such an assumption is questionable. Thus, statistical analyses are more reliable over the short to medium term, while RCMs are necessary to understand large warming over the long term. Nonetheless, in the long term RCMs remain driven by the input from GCMs, and so they do not resolve uncertainty related to climate variability, for example, that is produced by the GCMs.

11. **In almost all cases, downscaling improves our ability to reproduce the current climate, but it does not reduce the uncertainty on future changes.** Downscaling makes it possible to represent additional mechanisms and is likely to improve both the ability of represent the current and future climate (for instance, downscaling technique is required to look at precipitation in coastal or mountainous areas, or to look at small-scale phenomenon like heavy precipitations). But downscaling cannot help manage uncertainty if global climate models disagree. As in West Africa and the monsoon, downscaling can increase the resolution of scenario with an increase in precipitation scenario and a scenario with a decrease in precipitation, but the difference between both will not be resolved.

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