Climate-related hazards are causing significant damage and loss of life across the world. Timely evaluations of climate change hazards through systematic analysis are crucial for developing effective contingency plans. This study assessed climate change hazards in Devadurga, a disaster-prone sub-district in India, using the Fuzzy Analytic Hierarchy Process (FAHP) and a novel Composite Principal Component Analysis (CPCA). Thematic layers representing 10 flood-conditioning factors were developed and converted into fuzzy values using a linear fuzzy membership function in ArcGIS to generate a Flood Hazard Index (FHI). The findings revealed that 21 of the 189 villages along the Krishna River corridor were categorised within the very high flood-hazard zone. Analysis of 74 years (1950-2024) of hydro-climatic data revealed a mean annual rainfall of 733.02 mm (CV = 33.43%) and an average of 138 consecutive dry days annually. Extreme rainfall events (>= 20 mm) occurred 3 to 25 days yr(-)1, while rainfall >= 10 mm occurred 9 to 45 days yr(-)1. Standardised drought indices ranged from - 1.59 to 2.98, reflecting recurring episodes of moderate to extreme droughts. The Effective Precipitation-Streamflow Index (EPSI) derived from the Clayton copula demonstrated positive dependence (r = 0.42), with drought recorded in 41 of 74 years, including 12 severe drought events. The first principal component explained 61.27% of total variance among ten remote sensing drought indicators, forming the Composite Principal Component Analysis Drought Index (CPCADI) and revealing persistent agricultural drought stress. Thermal extremes were evident from a Warm Spell Duration Index (WSDI) of 28.8 days yr(-)1 and 230 tropical nights yr(-)1, indicating intensifying heat stress across the sub-district. The integration of FAHP with CPCA provides a robust quantitative and spatially explicit framework for multi-hazard assessment in semi-arid regions, offering valuable insights to support targeted climate adaptation planning.