<![CDATA[Climate change has altered rainfall characteristics and seasonal patterns. This matter affects mostly rainfed agricultural systems in karst regions. This study aims to analyze rainfall characteristics, extreme rainfall events, seasonal shifts, and the reliability of traditional seasonal knowledge (pranata mangsa) in the Rongkop karst area, Indonesia. The dataset used in this study are long-term daily rainfall data from Rongkop stations for the period 1986–2021. The rainfall data then convert to dasarian (10-day) temporal resolution. The daily rainfall was classified into intensity categories, while seasonal onset was determined based on rainfall thresholds. Trend analysis was conducted using the Mann–Kendall test and Sen’s slope estimator. The results indicate that Rongkop has moderate annual rainfall, with an average of 2012 mm, and no significant trend in total rainfall. However, there is a significant increase in the frequency of heavy rainfall events (>50 mm/day), indicating a shift towards higher rainfall intensity. Seasonal analysis shows variability in the onset of the rainy season between late August and late December, and the dry season between early February and mid-May. Seasonal duration also varies significantly, with extended dry spells and increased rainfall during the dry season. The results indicate increasing inconsistencies between traditional and observed rainfall patterns, with very high rainfall occurring during the dry season and very low rainfall during the wet season. The onset of the wet and dry seasons also exhibits significant variability beyond the expected seasonal boundaries. These findings suggest that although traditional seasonal forecasts (pranatamangsa) still reflect general climate patterns, their reliability for agricultural planning has declined due to increasing rainfall variability, extreme events, and large-scale climate phenomena such as El Niño and La Niña, particularly in rainfed karst areas. Therefore, integrating traditional seasonal knowledge with modern climatological analysis is crucial to support adaptive agricultural management, particularly in karst areas.]]>
