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Studies associated with climate change and variability are of great importance at both the global and local scale in the global climate crisis. In this study, change-point detection and trend analysis were carried out on mean, maximum, minimum air temperatures and total precipitation based on monthly, seasonal and annual scale in Bartın province located in the western Black Sea Region of Turkey. For this aim, 4-different homogenei-ty tests (von Neumann test, Pettitt test, Buishand range test and standard normal homogeneity test) for change-point detection, Modified Mann–Kendall test and Şen’s innovative trend test for trend analysis, and Sen’s slope test for the magnitude estimation of trends were used. According to the test results, the summer temperatures in particular show increasing trends at the 0.001 significance level. Mean maximum temperature in August, mean minimum temperature in June and August, and mean temperature in July and August are in increasing trend at the 0.001 significance level. Over a 51 year period (1965–2015) in Bartın province, the highest rate of change per decade in air temperatures is in August (0.55°C for Tmax, 0.46°C for Tmin and 0.43°C for Tmean) based on Sen’s slope. However, the study showed that apart from October precipitation, there is no significant trend in monthly, seasonal and annual precipitation in Bartın. Increasing trends in mentioned climate variables are also visually very clear and strong in Şen’s innovative trend method, and they comply with the statistical results. As a result, the study revealed some evidence that temperatures will increase in the future in Bartın and its environs.

This study was carried out to determine the methods that bear the most realistic results in predicting the number of fires and burned area under the climate conditions in future. Different indices and statistical methods were used in predicting the burned area and the number of fires. With this aim, in addition to the indices used in estimating the climate, Machine Learning and multivariate adaptive regression spline (MARS) models are also used in predicting these factors. According to the results obtained in several studies, the relationship between the drought and fire indices burned area and the number of fires changes from region to region. While better results are obtained in predicting the burned area and the number of fires via the drought indices being used in this study and the MARS models that the combinations of these indices use, it is seen that a 30-39% success was achieved for predicting the amount of burned area via Machine Learning methods (Kernel Nearest Neighbor (kNN), Recursive Partitioning and Regression Trees (RPART), Support Vector Machine (SVM) and RF), and this success ranges widely from 8 to 41% in terms of the number of fires. RPART, of these four algorithms, performed the best in fire prediction, but kNN was the worst.