The flood-drought severity in arid regions such as Xinjiang is increasingly influenced by climate extremes. While prior studies have explored the relationship between climate extremes and flood-drought dynamics, few have analyzed these interactions at different time and spatial scales using different method combinations. This study addresses that gap by utilizing a gridded dataset (CN05.1) during 1961-2020, examining the China Z index (flood-drought index) and climate extremes. The analysis reveals significant increases in precipitation and heat extremes, while cold extremes have decreased. In addition to overall periodic changes with 2.5 and 8 years in the flood-drought severity, our results demonstrate a significant spatial shift between 1981 and 2000 and between 2001 and 2020. Previously flood-dominant regions, including portions of the Junggar Basin, Eastern Tianshan Mountains, and Tarim River Basin, transitioned to drought-dominant in 2001-2020. Conversely, drought-dominant regions became flood-dominant. Strong positive correlations (0.65-0.84) were found between the Z index and precipitation extremes, while temperature extremes showed weaker correlations. Furthermore, we applied six variable selection regression methods, with Random Forest variable selection + Random Forest regression (RF+RF) performing the best (mean R2 = 0.71), highlighting their ability to manage non-linear relationships and multicollinearity between climate indices. RF+RF proved more effective at handling correlated variables, which were crucial in capturing the region's flood-drought dynamics. The quantified spatial reversals and non-linear climate-flood/drought relationships provide actionable metrics for early warning systems, enabling targeted infrastructure upgrades and water allocation policies in arid regions. These findings establish a transferable framework linking climate extremes to hydrological risks, directly informing adaptive land management and disaster preparedness strategies for Xinjiang and analogous regions under intensifying climate variability.