Multisource Data and Explainable Machine Learning for Monitoring Compound Drought and Heat Events at Fine Scale in a Typical Arid and Semiarid Region

Compound drought and heat events (CDHEs) have been intensifying under global climate change, posing significant threats to arid and semiarid regions and underscoring the imperative of precise regional monitoring. However, most existing studies focus on single hazards; even in those addressing CDHEs, the compound indices employed are typically constrained by coarse spatial resolution (exceeding 0.1°), which hinders fine-scale evaluations. To address the above issue, this study proposes a novel framework for developing the high-resolution compound drought and heat index (HCDHI), an indicator designed to finely reflect CDHEs. Specifically, three machine learning (ML) models [extreme gradient boosting (XGBoost), support vector regression (SVR), and artificial neural network (ANN)] are employed to fit station-based standardized precipitation evapotranspiration (ET) index (SPEI) and standardized temperature index (STI) values, using features extracted from remote sensing and reanalysis data. The best-performing model is interpreted via the SHapley Additive exPlanations (SHAP) method for transparency and then applied to generate 1-km-resolution SPEI and STI datasets. The HCDHI is subsequently obtained by integrating the SPEI and STI datasets using the copula function. When applied to Ningxia, a typical arid and semiarid region of China, during 2001–2022, the XGBoost model demonstrated superior performance ( $R^{2}=0.70/0.72$ and root-mean-square error (RMSE) = 0.56/0.51), closely aligning with observed data. SHAP analysis identified the precipitation (27.2% of total importance) as the primary contributor to the drought monitoring model and the land surface temperature (LST) (22.1%) as the key factor to the heat monitoring models, respectively; potential ET (PET), vapor pressure deficit (VPD), and LST (each exceeding 10%) also contributed substantially to drought–heat interactions. Monitoring results indicated that the CDHEs affect a larger area and exhibit greater severity than single hazards. The proposed high-resolution HCDHI significantly enhances the capacity for CDHEs monitoring and facilitates fine-scale assessment, particularly in data-scarce regions.