This study analyzes the mean, maximum, and minimum temperatures and precipitation trends in southeast Mexico-Yucatan Peninsula, Central America and the Caribbean regions. The Climate Research Unit (CRU) TS 4.01, with a spatial resolution of 0.5° × 0.5°, was the database used in this research. The trends of the four selected climate variables cover the period from 1960 to 2016. The results obtained show a clear and consistent warming trend, at a rate of about 0.01 °C/year for the entire study region. These results are consistent with some previous studies and the IPCC reports. While the trends of precipitation anomalies are slightly positive (~0.1 mm/year) for southeast Mexico-Yucatan Peninsula and almost the entire Caribbean, for Central America (CA) the trends are negative. The study also presents the correlation between temperatures and precipitation versus El Niño Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), and Atlantic Multidecadal Oscillation (AMO) drivers, indicating global warming and frequency signals from the climate drivers. In terms of the near future (2015–2039), three Representative Concentration Pathways (RPC) show the same trend of temperature increase as the historical record. The RCP 6.0 has trends similar to the historical records for CA and southeast Mexico-Yucatan Peninsula, while the Caribbean corresponds to RCP 4.5. In terms of the far-future (2075–2099), RCP 6.0 is more ad-hoc for southeastern Mexico-Yucatan Peninsula, and RCP 8.5 corresponds to Central America. These results could help to focus actions and measures against the impacts of climate change in the entire study region.

This study aimed to determine the mean temperature trends in the south-southeast region of Mexico during the historical period of 1980–2014, as well as during the future periods of 2021–2040, 2041–2060, and 2081–2100, as recommended by the IPCC. Additionally, the study sought to identify the climate change scenario that is most closely aligned with the socio-environmental conditions of the south-southeast zone of Mexico and that has the greatest impact on the region’s average temperature. The downscaling method of bias correction was conducted at a spatial resolution of 0.25° × 0.25°, and an analysis of historical trends was performed for the period 1980–2014 with ERA5 and four CMIP6 models (CNRM-ESM2-1, IPSL-CM6A-LR, MIROC6, and MRI-ESM2-0). This process was extended to future projections. The models indicated temperature differences of less than 0.5 °C with respect to ERA5, in agreement with other studies. Additionally, the current study calculated future trends for the south-southeast region using three of the CMIP6 scenarios (SSP2-4.5, SSP4-6.0, and SSP5-8.5). The z-eq proposal was used to compare the slopes, enabling us to determine which of the three scenarios corresponded to the historical trend, assuming identical socio-environmental conditions. The SSP4-6.0 scenario was found to correspond to the historical trend.

This study analyzes 50 annual climate change indices related to temperature and precipitation in the Valley of Mexico basin for the period 1951–2010. First, a quality and homogenization analysis of 90 weather stations (categorized as urban, suburban, and rural) in the basin was performed using the Climatol algorithm. The non-parametric Mann–Kendall test and the Sen’s slope method were applied to determine the existence of a trend and to estimate the magnitude of the change in extreme climate indices, respectively. To eliminate the serial correlation problem, the lag-1 method and the Patakamuri tests were used. Statistically significant positive trends were found for SU, TMm, TNm, TNn, TX90p, and WSDI, as well as negative ones for FD, TX10p, TN10p, CSDI, and HDDheat18. The results seem to support an influence of anthropogenic global warming on the study region, rather than local effects of urbanization. However, it is likely that some significant differences in the urban change rate of some indices could be due to local effects, such as the difference in land cover that occurs between urban and rural stations. Not enough statistically significant results were found for the climate change indices related to precipitation in most of the stations. Compared to other studies in the Mexico City area, the main contribution of this study is the analysis of 50 climate indices in a 60-year period working with a quality-controlled and homogenized database.

One of the main current challenges is detecting changes in the climate at the regional level. The present study tried to address this issue by looking for some influence of large-scale climate oscillations on the climate of a small and complex topography basin in Central Mexico. We collected temperature and precipitation data from 44 climate stations within an area of up to 20 km around the Apatlaco River sub-basin (~30 km south of Mexico City) during the period 1950–2013. Posteriorly, quality analysis and homogenization of the climate databases were performed by using the Climatol algorithm. We analyzed the trend of five ETCCDI climate indices through several statistical tests. Finally, we calculated simple Pearson correlations of those indices with four climate oscillation indices that have affected Mexico’s climate in the recent past. The results revealed that the Atlantic Multidecadal Oscillation had a clear influence on four of the five indices analyzed in the study area. The summer days and the extreme maximum and minimum temperatures accounted for a small increase in the temperature of the middle east (urban) basin compared to the middle west (rural), which could be a manifestation of the heat island effect or the difference in soil type (and therefore albedo) of the two zones. As expected, the midsummer drought effect predominated in most of the sub-basin, with only the uppermost part showing monsoon-type precipitation during a typical year.

The advancements in global climate modeling achieved within the CMIP6 framework have led to notable enhancements in model performance, particularly with regard to spatial resolution. However, the persistent requirement for refined techniques, such as dynamically or statistically downscaled methods, remains evident, particularly in the context of precipitation variability. This study centered on the systematic application of a bias-correction technique (quantile mapping) to four designated CMIP6 models: CNRM-ESM2-6A, IPSL-CM6A-LR, MIROC6, and MRI-ESM2-0. The selection of these models was informed by a methodical approach grounded in previous research conducted within the southern–southeastern region of Mexico. Diverse performance evaluation metrics were employed, including root-mean-square difference (rmsd), normalized standard deviation (NSD), bias, and Pearson’s correlation (illustrated by Taylor diagrams). The study area was divided into two distinct domains: southern Mexico and the southeast region covering Tabasco and Chiapas, and the Yucatan Peninsula. The findings underscored the substantial improvement in model performance achieved through bias correction across the entire study area. The outcomes of rmsd and NSD not only exhibited variations among different climate models but also manifested sensitivity to the specific geographical region under examination. In the southern region, CNRM-ESM2-1 emerged as the most adept model following bias correction. In the southeastern domain, including only Tabasco and Chiapas, the optimal model was again CNRM-ESM2-1 after bias-correction. However, for the Yucatan Peninsula, the IPSL-CM6A-LR model yielded the most favorable results. This study emphasizes the significance of tailored bias-correction techniques in refining the performance of climate models and highlights the spatially nuanced responses of different models within the study area’s distinct geographical regions.

El cambio climático es un fenómeno que, en la actualidad, presenta un desafío para los países a nivel mundial. Los efectos, que este fenómeno puede tener, están asociados a cambios en las variables meteorológicas por largos periodos. Esto ha llevado a desarrollar métodos para proyectar estos cambios y tomar las medidas necesarias de mitigación y adaptación. En este trabajo se analizaron las proyecciones de temperatura máxima (Tmáx) por un período de 25 años en un punto específico en la ciudad de Villahermosa, Tabasco. Las mediciones se obtuvieron de una estación de calidad del aire. Se empleó un escenario de las trayectorias de concentración representativa (RCP-siglas en inglés), para encontrar un patrón de comportamiento futuro de Tmáx. El análisis del RCP 6.0 mostró que se espera un aumento promedio de Tmáx de 1.34 °C a lo largo de 2015-2039, con una tasa de 0.02 °C/año, similar con la histórica reportada en otros estudios.