| Sumario: | Global Climate Models (GCMs) have been the primary source of information for constructing climate scenarios, and they provide the basis for climate change impacts assessments of climate change at all scales, from local to global. However, impact studies rarely use GCM outputs directly because errors in GCM simulations relative to historical observations are large (Ramirez-Villegas et al. 2013), and because the spatial resolution is generally too coarse to satisfy the requirements for finer-scale impact studies. More specifically, the typical GCM spatial resolution (50 km or even more) is not practical for assessing agricultural landscapes, particularly in the tropics, where orographic and climatic conditions vary significantly across relatively small distances (Tabor & Williams, 2010). Hence, it is important to bias-correct and downscale the raw climate model outputs in order to produce climate projections that are better fit for agricultural modeling. Here we describe three different calibration approaches to produce reliable daily climate for future periods, employed in a new interface in CCAFS-Climate portal (www.ccafs-climate.org/data_bias_corrected/), as follows: (a) bias correction (or nudging) (Hawkins et al., 2013), (b) change factor (Hawkins et al., 2013) and (c) Quantile Mapping (Gudmundsson et al., 2012). In addition, briefly describe some observational datasets relevant to agricultural modeling and employed as the historical observations for the calibration methods mentioned here.
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