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International audience; The evaluation of different climate models' projections is the basis for a probabilistic impact assessment of climate change taking place in many regions of the world. We propose an original evaluation of the climate models through a weather typing approach. Such approaches have become common before a downscaling procedure of climate model output to a regional scale. The main underlying assumption is that the weather types are essentially the same in the training and the simulation periods, only the frequency of their occurrence might change. To verify this assumption, we define weather types for several models from the IPCC models database for a training period. Those are then compared to a) weather types obtained from future simulations from the same models and b) to weather types obtained in the same manner for reanalyses (NCEP/ERA40) for the same time period. On the one hand such an analysis provides means to test the validity of the assumption of weather patterns remaining constant between present and future climate (a). On the other hand the potential for reproducing reanalysis patterns in present climate can be quantified and compared (b). This evaluation of different models can not only be used to verify the main underlying assumption but also to select appropriate climate models for a downscaling procedure and a subsequent probabilistic impact assessment of changing precipitation patterns in several regions of the world.

International audience; The potential change in precipitation patterns with climate change mainly affects water resources and agriculture. This is of major concern in highly vulnerable regions as, e.g., in the Amazonian Basin. To plan relevant measures of adaptation, stakeholders profit from information about likely changes in water availability and regional precipitation patterns. The latter can be provided by a localised downscaling of climate model scenario simulations to given regions. A typical approach is weather typed downscaling: present climate's typical synoptic situations, so called weather types, are identified from reanalyses and linked to rainfall probabilities. These weather types are assumed to be present also in a future climate, basically unchanged but with a different frequency of occurrence. Together with their associated rainfall probabilities, they provide the basis for a downscaling concept. On the way towards such a weather typed downscaling approach, we relate characteristics of extreme rainfall observations from various rain gauges in the Amazonian Basin to reanalyses weather types identified in that area.

The annual cycle of extreme 1-day precipitation events across the UK is investigated by developing a statistical model and fitting it to data from 689 rain gauges. A generalized extreme-value distribution (GEV) is fit to the time series of monthly maxima, across all months of the year simultaneously, by approximating the annual cycles of the location and scale parameters by harmonic functions, while keeping the shape parameter constant throughout the year. We average the shape parameter of neighbouring rain gauges to decrease parameter uncertainties, and also interpolate values of all model parameters to give complete coverage of the UK. The model reveals distinct spatial patterns for the estimated parameters. The annual mean of the location and scale parameter is highly correlated with orography. The annual cycle of the location parameter is strong in the northwest UK (peaking in late autumn or winter) and in East Anglia (where it peaks in late summer), and low in the Midlands. The annual cycle of the scale parameter exhibits a similar pattern with strongest amplitudes in East Anglia. The spatial patterns of the annual cycle phase suggest that they are linked to the dominance of frontal precipitation for generating extreme precipitation in the west and convective precipitation in the southeast of the UK. The shape parameter shows a gradient from positive values in the east to negative values in some areas of the west. We also estimate 10-year and 100-year return levels at each rain gauge, and interpolated across the UK.