For future climate projections to be useful they must be actionable at the local level. In this study, we develop daily temperature and precipitation climate scenarios suitable for use in projections of drought, energy use, water use, and crop production. We investigate the magnitude of future changes to air temperature and precipitation in the Midwest United States in response to three future climate change scenarios. Results are used to assess changes to incidence of precipitation extremes and human comfort (using heat index) associated with the anticipated climate changes in the region.We use self-organizing maps and random forest based techniques to generate daily realizations of temperature and precipitation for 279 weather stations in a region centred on Illinois. We determine that the random forest model performs best for maximum and minimum temperatures, while the self-organizing map performs best for precipitation. Using nine models from the Coupled Model Inter-Comparison Project Phase 5, downscaled daily temperature and precipitation values are generated for low, moderate, and high greenhouse gas emissions scenarios for historical and future periods. Based on recent trends, we focus our results on the high emissions scenario, and show an average increase of 4.3 C in maximum daily air temperature across the region for the 2071-2100 period. Precipitation decreases by up to 15% in the southern half of the study region, with a similar percentage increase in the northern half of the region. The regional environmental changes result in an increase of 5.8 in average summer heat index, and increase of 48% in the number of days likely to produce extreme heat, and a decrease in the average value of the standardized precipitation and evapotranspiration index of 1.9 (indicating increased drought) across the region by 2100.

The West Africa region (5to 20N and 10E to 20W) is particularly vulnerable to climate change due to a combination of unique geographic features, meteorological conditions, and socio-economic factors. Drastic changes in precipitation (e.g., droughts or floods) in the region can have dramatic impacts on rain-fed agriculture, water availability, and disease risks for the region's population. Quantifying these risks requires localized climate projections at a higher resolution than is generally available from global climate models. Using Self-Organizing Maps, we produce station-based downscaled precipitation projections for medium and high-emission climate scenarios for this region. We find slight increases in precipitation in the coastal areas, and decreases in the interior Sahel region by an average of 10\% by 2100 under the high greenhouse gas-emission scenario of Shared Socioeonomic Pathway 5-8.5. Precipitation decreases in the Sahel are primarily driven by reductions in the number of rainy days during the wet season, rather than by consistent decreases in the magnitude of the precipitation amounts or decreases in the average length of the wet season.

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Abstract. Statistical downscaling methods provide an essential bridge between low resolution global climate models and localized information needed by decision makers. As the demand for localized climate information continues to grow to make projections for a wide variety of applications, the need for software that can provide this sort of downscaled data grows with it. The CCdownscaling package described in the article provides a number of downscaling methods, including Self Organizing Maps, as well as a number of evaluation metrics for assessing downscale model skill. In this article, we describe the features of the CCdownscaling package, and show an example use case for downscaling temperature and precipitation. It is open-source and freely available for use in generating downscaled projections.