Monthly precipitation trends in Türkiye under complex topography and varying climate: a seasonal Mann–Kendall and innovative polygon trend framework

The impacts of climate change are increasingly sensible, and Türkiye with its varied topography and diverse climate types is particularly exposed to these shifts. Understanding how precipitation patterns are evolving across its regions is therefore critical. While trend analysis is a widely used tool for this purpose, conventional approaches overlook periodicity, limiting their practical value for water resource management and irrigated agriculture. Therefore, this study aims to examine seasonal trends in long-term (1969–2020) monthly total precipitation in Türkiye, in which each calendar month is treated as a separate season in the sense of seasonal trend-analysis terminology, accounting for altitude, slope, geographical region, and Köppen climate classification. For this purpose, the seasonal Mann-Kendall (SMK) test and the innovative polygon trend Wilcoxon significance test (IPWST), a relatively new graphical method, were used, and the two approaches were comparatively evaluated. According to the study’s results, the rate of increase in coastal areas, specifically the Aegean and Black Seas, exceeds the rate of decrease. In contrast, the reverse is observed in inland and eastern regions. The IPTWST method indicates a more pronounced increasing trend in the Marmara and Mediterranean regions. Furthermore, although analogous trends are observed at lower slopes and altitudes, analyses indicate that these trends are more strongly related to altitude, with the rate of increase decreasing as altitude increases. The IPTWST identified significant or weak trend signals at a larger number of stations than the SMK, particularly in relation to altitude. Nationwide, the two methods revealed significant increases of 7% and decreases of 6%, respectively; this pattern was supported by statistical tests, reflecting the variability of regional climates and the influence of topographic factors on trend analysis.