# Regional Climate Change in Southeast Mexico-Yucatan Peninsula, Central America and the Caribbean
## 2 Materials and Methods
### Data  
The historical data is obtained from the Climate Research Unit (CRU) monthly database CRUTS4.01, which is a regular grid with a spatial resolution of 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\)[47]. The chosen climate variables are precipitation (Pre) and median, minimum, and maximum temperatures (Tmp, Tmx, Tmn) during the 1960-2016 period. The advantage of the CRU data over the observation data is that it provides information on the overall study time records with high-quality control in the regions with a lack of data [7]. Spatial interpolation from weather station observations is used to achieve spatially complete data, with the number of nearby stations used as an indicator of data reliability [47]. CRU data has been improved for climate change analyses [2; 44]. Historically, CRU has been widely used as a verification dataset of GCM historical simulations [48; 49; 50; 51; 52], including some examples in SM-YP, CA and the Caribbean [49; 53; 54]. The model's data related to climate change are taken from the Intergovernmental Panel on Climate Change, obtained from CMIP5 [55]. They are listed in Table 1. These data were processed by [56], using the Reliability Ensemble Averaging method (REA) [57]. These data have the same spatial resolution as the CRU data and cover periods of 25 years (2015-2039 and 2075-2099). The REA method has two criteria to evaluate each model used and generates projections by assembling those models. The criteria are known as performance criteria (in terms of the historical part) and convergence criteria (in terms of the future part), which are convolutionary to obtain the overall evaluation of the model by the grid point at the regional level [9; 47; 57; 58; 59]. REA is a flexible tool that can be used for both global and regional scale studies [60]. REA has been applied at the continental scale [57], in grid boxes of 2.5\({}^{\circ}\)\(\times\) 2.5\({}^{\circ}\) and 1\({}^{\circ}\)\(\times\) 1\({}^{\circ}\)[60] and in small subregions in the northeastern United States. The criteria for selecting the study points are based on human settlements where climate change is a stressor for the population. Furthermore, these study points have been selected at a point close to a close city. This criterion has been the same for the SM-YP, CA and the Caribbean (the main islands) (Figure 2 and Table 2). In addition, the selected points were extracted from both gridded data (CRU and REA method). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Id** & **Models** & **Scenarios RCP** \\ \hline
1 & BCC-CSM & 4.5, 6.0, 8.5 \\ \hline
2 & CanESM2 & 4.5, 8.5 \\ \hline
3 & CNRM-CM5 & 4.5, 8.5 \\ \hline
4 & CSIRO-MK3-6-0 & 4.5 \\ \hline
5 & GFDL & 6.0, 8.5 \\ \hline
6 & GISS-E2-R & 4.5, 6.0, 8.5 \\ \hline
7 & HadGEM2-ES & 4.5, 6.0, 8.5 \\ \hline
8 & inmcm4 & 4.5, 8.5 \\ \hline
9 & IPSL-CM5A-LR & 4.5, 6.0, 8.5 \\ \hline
10 & MIROC5 & 4.5, 6.0, 8.5 \\ \hline
11 & MIROC-ESM-CHEM & 4.5, 6.0 \\ \hline
12 & MIROC-ESM & 4.5, 6.0, 8.5 \\ \hline
13 & MPI-ESM-LR & 4.5, 8.5 \\ \hline
14 & MRI-CCGM3-M & 4.5, 8.5 \\ \hline
15 & NorESM1-M & 4.5, 6.0, 8.5 \\ \hline \hline \end{tabular} Source. [[https://pcmdi.llnl.gov/mips/cmip5/](https://pcmdi.llnl.gov/mips/cmip5/)]([https://pcmdi.llnl.gov/mips/cmip5/](https://pcmdi.llnl.gov/mips/cmip5/)) (accessed date: 31 August 2021).  
\end{table} 
Table 1: Fifteen Global Coupled Models from CMIP5-IPCC.