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A statistical method is developed to generate local cumulative distribution functions (CDFs) of surface climate variables from large‐scale fields. Contrary to most downscaling methods producing continuous time series, our “probabilistic downscaling methods” (PDMs), named “CDF‐transform”, is designed to deal with and provide local‐scale CDFs through a transformation applied to large‐scale CDFs. First, our PDM is compared to a reference method (Quantile‐matching), and validated on a historical time period by downscaling CDFs of wind intensity anomalies over France, for reanalyses and simulations from a general circulation model (GCM). Then, CDF‐transform is applied to GCM output fields to project changes in wind intensity anomalies for the 21st century under A2 scenario. Results show a decrease in wind anomalies for most weather stations, ranging from less than 1% (in the South) to nearly 9% (in the North), with a maximum in the Brittany region.

[1] Reanalysis data and general circulation model outputs typically provide information at a coarse spatial resolution, which cannot directly be used for local impact studies. Downscaling methods have been developed to overcome this problem, and to obtain local-scale information from large-scale atmospheric variables. The deduction of local-scale extremes still is a challenge. Here a probabilistic downscaling approach is presented where the cumulative distribution functions (CDFs) of large-and local-scale extremes are linked by means of a transfer function. In this way, the CDF of the local-scale extremes is obtained for a projection period, and statistical characteristics, like return levels, are inferred. The input series are assumed to be distributed according to an extreme value distribution, the Generalized Pareto distribution (GPD). The GPD parameters are linked to further explanatory variables, hence defining a nonstationary model. The methodology (XCDF-t) results in a parametric CDF, which is as well a GPD. Realizations generated from this CDF provide confidence bands. The approach is applied to downscale National Centers for Environmental Prediction reanalysis precipitation in winter. Daily local precipitation at five stations in southern France is obtained. The calibration period 1951-1985 is used to infer precipitation over the validation period 1986-1999. The applicability of the approach is verified by using observations, quantile-quantile plots, and the continuous ranked probability score. The stationary XCDF-t approach shows good results and outperforms the nonparametric CDF-t approach or quantile mapping for some stations. The inclusion of covariate information improves results only sometimes; therefore, covariates have to be chosen with care.

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Abstract. Rhipicephalus sanguineus, the brown dog tick, has a worldwide distribution in areas with a relatively warm climate, including mild winters. This tick plays an important role as vector for various animal and human pathogens, including bacteria and protozoa. Based on precise daily meteorological data from the past 40 years, combined with mathematical modelling designed to predict tick activity, two modelling approaches were developed. The first examined the evolution of the number of weeks with favourable biological conditions for ticks in four French cities located at various latitudes of the country: Nîmes in the south, Paris in the north, Lyon in the east and Nantes in the west. The second analysed the extension of the geographical surface area in km 2 where the biological conditions favour tick activity for at least 12 weeks per year. Both analyses revealed clear evidence of increased temperatures coupled with an augmented tick activity index in three of the four cities. However, the change was not significant in Nîmes, where the climate is Mediterranean and the tick is already endemic. For Paris, Lyon and Nantes, the activity index values have increased significantly, i.e. by 4.4%, 4.0% and 3.4%, respectively. The distribution of the activity index values is evolving strongly with significantly fewer values below 50% since the 1960s and a clear decrease of values between 20% and 50% during the latest decade. Between 1960 and 2000, the theoretical extension of the surface area where the climatic index is suitable for R. sanguineus has increased by 66%. Even though several other important factors, such as changes in biotopes or human activity, are not included in this study, the resulting patterns and trends are noticeable. Our models constitute the first demonstration of the impact of climate change on the activity and distribution of ticks and confirm the observed northward migration trend for this Mediterranean domestic tick.