Provides a unified workflow for applying Explainable Artificial Intelligence (XAI) methods to hydro-climate predictive models. Functions implement a permutation-based Monte Carlo SHAP estimator (Strumbelj and Kononenko (2014) <doi:10.1007/s10115-013-0679-x>; Lundberg and Lee (2017) <doi:10.48550/arXiv.1705.07874>), a self-contained locally weighted linear surrogate LIME (Ribeiro et al. (2016) <doi:10.1145/2939672.2939778>), and Partial Dependence Plots with Accumulated Local Effects (Friedman (2001) <doi:10.1214/aos/1013203451>; Apley and Zhu (2020) <doi:10.1111/rssb.12377>) with hydrology-specific visualisations and interpretation utilities. Supports any model object compatible with the 'DALEX' explainer interface (Biecek (2018) <doi:10.18637/jss.v097.i01>), including random forests, gradient boosting, and neural networks trained on streamflow, drought indices, flood risk, or evapotranspiration data. Hydrology-standard performance metrics Nash-Sutcliffe Efficiency (NSE, Nash and Sutcliffe (1970) <doi:10.1016/0022-1694(70)90255-6>) and Kling-Gupta Efficiency (KGE, Gupta et al. (2009) <doi:10.1016/j.jhydrol.2009.08.003>) are computed alongside standard regression metrics. Designed to accompany the book chapter: Islam, S., Dheeraj, A., Ali, S., Kaushal, R. and Venkatesh, G. (2026). Explainable Artificial Intelligence for Hydro-Climatic Modelling: Methods, Applications, and Implementation Using the xaiHydro R Package. In Chandniha, S. K. et al. (Eds.), Hydro-Climate Analytics: Remote Sensing, AI and Geospatial Modelling. Springer.