Background: It has been suggested that emerging arboviral diseases, such as chikungunya and Zika, have been at least partially driven not just by migration and social factors, but also by climate change and variability. In the case of Latin America in 2014 occurred the introduction of the Asian genotype which led to the subsequent epidemics in the region, including Colombia. The, we were interested in assess the influence of climate in an Aedes-endemic region of Colombia where CHIK was introduced in early 2015, the Coffee-Triangle region (constituted by 53 municipalities), regard the epidemiology of CHIK during 2015-2016 epidemics. Methods & Materials: Epidemiological surveillance data (weekly cases) were collected, and incidence rates were calculated. Poisson regression models were used to assess the influence of the macroclimatic variable ONI (Oscillation Niño Index) on the CHIK incidence rate, adjusting by year and week. Monthly satellite images for total rainfall and surface temperature were obtained from the Tropical Rainfall Measuring Mission (1 month–TRMM) imagery database from NASA Earth Observations (NEO, NASA, USA) (http://neo.sci.gsfc.nasa.gov/) and were analyzed with Google Earth® software. Results: A total of 15,785 cases of CHIK were identified. Quindio department concentrated 6,404 of them (1,045.17 cases/100,000 pop). La Virginia, Risaralda department, was the municipality with the highest cumulated incidence, 3,770 cases/100,000 pop). At nonlinear regressions, significant associations were found with ONI (p < 0.05), at 35 out of 53 municipalities of the region, being higher at: Genova (r2 = 0.4986), Belen de Umbría (r2 = 0.4902), Dosquebradas (r2 = 0.4270), Pereira (r2 = 0.3926) Calarca (r2 = 0.3478), Santuario (r2 = 0.3259) and La Tebaida (r2 = 0.3253). For the whole region was r2 = 0.2834. Conclusion: El Niño significantly affected the incidence of chikungunya in the region. This association with climate change and variability should be considered in the elements influencing disease epidemiology and pathogens emergence. In addition, predictive models should be developed further with more available data from disease surveillance. This information should also be considered for studies relating to climate change and vector control.