pyClim-SDM: Service for generation of statistically downscaled climate change projections supporting national adaptation strategies

The climate change impact and adaptation communities need future scenarios with sufficient high resolution, which are frequently achieved by applying Statistical Downscaling Models (SDMs) over global climate models. A large variety of SDMs exists, and some can be more suitable than others for each specific purpose. For this reason, it is important to develop tools to facilitate the evaluation and generation of downscaled scenarios following different approaches. In this paper we present a service, 'pyClim-SDM', which allows users to generate and evaluate their own downscaled scenarios with a very simple and user-friendly graphical interface. This tool includes a large collection of state-of-the-art methods belonging to different families to downscale daily data of the following surface variables: temperature, precipitation, wind, relative humidity and cloud coverage. Additionally, the software is prepared to be applied over any other user-defined target variable. Thus, multivariable indexes can be tackled as target variables themselves, instead of being calculated from the downscaled primary variables. With this possibility, potential intervariable inconsistencies are avoided. An application example for a Fire Weather Index, dependent on temperature, wind, humidity and precipitation, is shown. The service here presented-mainly based on a new downscaling software and a user-friendly graphical interface-is an essential piece for evaluating and generating high-resolution projection data within the Spanish national climate change adaptation strategy which includes, among other elements, a common database for all sectors, viewer and data distribution portal, etc.