Lightning is a primary driver of severe convective hazards and wildfire ignitions, yet long-term, high-resolution gridded records have remained scarce due to the limited temporal coverage of ground-based networks and the sampling constraints of satellite observations. Here, we presented a new global 0.25° × 0.25° monthly land lightning stroke-density dataset spanning 1979–2025. To ensure robustness, we developed a ridge regression stacking ensemble that integrated four complementary machine learning architectures: eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Random Forest (RF), and Deep Neural Network (DNN). The ensemble achieved superior performance over each single model (test R² = 0.6895, RMSE = 0.0108, MAE = 0.0030), indicating that model blending effectively enhanced predictive stability. Individual validations confirmed high spatial fidelity, as the ensemble successfully reproduced the observed large-scale spatial distribution and major tropical–subtropical continental lightning hotspots. Independent comparisons with the LIS/OTD gridded lightning climatology (±38°) further demonstrated strong spatiotemporal consistency, particularly in reproducing interannual variability. Our analysis revealed pronounced regional heterogeneity in multi-decadal trends: significant decreases were concentrated across several tropical convective centers, while localized increases emerged in specific mid-latitude regions. Attribution based on SHapley Additive exPlanations (SHAP) elucidated that these patterns were primarily governed by the coupling of thermodynamic instability (CAPE × TP), moisture availability, and ice-phase hydrometeor conditions. This dataset provided a physically constrained and spatially detailed basis for studying long-term lightning dynamics, offering practical inputs for natural-ignition modeling, lightning-produced NOx estimation, and the evaluation of lightning parameterizations in climate and Earth system models. The datasets of the 1979–2025 Global Land Lightning Density Reconstruction Version 1 (GLLDR v1) are publicly available at the Zenodo via the following DOI: <a href="https://doi.org/10.5281/zenodo.19722380" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.19722380</a> (Zheng et al., 2026a).