Groundwater is a vital resource for millions of people, especially in arid and semi-arid regions where surface water is scarce. Effective management of this resource requires accurate monitoring of groundwater levels. However, traditional monitoring methods are often costly and technically demanding, posing challenges in data-scarce regions. This study presents a low-cost, in-situ groundwater monitoring methodology tailored to northern Nigeria's Hadejia-Jama'are River Basin (HJRB). The methodology involves community engagement and the use of simple, affordable tools to collect reliable groundwater-level data. Over one-year, weekly measurements were taken from 42 shallow wells across the basin, capturing seasonal and spatial variations. Statistical analysis of the data reveals a significant degree of variability in groundwater levels, as reflected in standard deviations (SDs) ranging from 5.27 to 5.69 across the months of 2023. This variability is influenced by seasonal shifts, geographical differences in recharge rates, and the groundwater abstraction activities. Areas of higher SD highlight regions with more dynamic water table conditions, which could complicate water resource management and necessitate localized management strategies. This in-situ data are intended for future validation of remote sensing-based groundwater models, aiming to enhance the accuracy of large-scale groundwater monitoring. The study demonstrated the significance of integrating low-cost monitoring techniques with advanced technologies to improve water resource management in data-scarce regions. Understanding the variability in groundwater levels, as indicated by the SDs, will allow for more accurate calibration of remote sensing models and better resource allocation. Recommendations for future research include refining the methodology, expanding its application to other regions, and sustaining community engagement to ensure long-term success.

General circulation models (GCMs) underpin climate change assessments, yet their coarse spatial resolution and systematic biases constrain their direct use in regional applications. Post-processing approaches such as bias correction and statistical downscaling are therefore widely applied, yet these steps are often implemented independently, leading to inconsistencies between corrected statistics and spatial structure. This study presents a reproducible two-stage framework that integrates Quantile Delta Mapping (QDM) for bias correction with a deep learning-based super-resolution method to improve the statistical fidelity and spatial detail of climate model outputs. The framework is evaluated using precipitation, runoff, and evapotranspiration from three Coupled Model Intercomparison Project Phase 6 (CMIP6) GCMs (Canadian Earth System Model (CanESM5), Hadley Centre Global Environment Model (HadGEM3‐GC31‐LL), and Max Planck Institute Earth System Model (MPI‐ESM1‐2‐HR)), over the Hadejia-Jama'are River Basin in northern Nigeria. We first demonstrate that raw model outputs exhibit substantial biases, with domain-averaged root mean square errors (RMSE) of 48.6–57.3 mm month⁻¹ for precipitation, 0.93–7.51 mm month⁻¹ for runoff, and 33.8–58.7 mm month⁻¹ for evapotranspiration. QDM substantially reduces systematic errors, lowering precipitation RMSE to 23.8–27.8 mm month⁻¹, runoff RMSE to 0.24–1.85 mm month⁻¹, and evapotranspiration RMSE to 3.7–4.3 mm month⁻¹, while preserving projected distributional changes, as confirmed by Kolmogorov-Smirnov (D ≤ 0.072) and Wasserstein (≤ 11.86) metrics. In the second stage, a conditional Generative Adversarial Network (GAN) super-resolves the bias-corrected precipitation fields from ~250 km to 14 km (regridded to 1 km), outperforming bilinear and bicubic interpolation in terms of structural similarity and spatial coherence. The proposed QDM-GAN framework is fully documented and reproducible, with openly available code and data sources, and is intended as a modular post-processing tool that can support downstream modeling applications requiring bias-corrected, high-resolution climate inputs.

General circulation models (GCMs) underpin climate change assessments, yet their coarse spatial resolution and systematic biases constrain their direct use in regional applications. Post-processing approaches such as bias correction and statistical downscaling are therefore widely applied, yet these steps are often implemented independently, leading to inconsistencies between corrected statistics and spatial structure. This study presents a reproducible two-stage framework that integrates Quantile Delta Mapping (QDM) for bias correction with a deep learning-based super-resolution method to improve the statistical fidelity and spatial detail of climate model outputs. The framework is evaluated using precipitation, runoff, and evapotranspiration from three Coupled Model Intercomparison Project Phase 6 (CMIP6) GCMs (Canadian Earth System Model (CanESM5), Hadley Centre Global Environment Model (HadGEM3‐GC31‐LL), and Max Planck Institute Earth System Model (MPI‐ESM1‐2‐HR)), over the Hadejia-Jama'are River Basin in northern Nigeria. We first demonstrate that raw model outputs exhibit substantial biases, with domain-averaged root mean square errors (RMSE) of 48.6–57.3 mm month⁻¹ for precipitation, 0.93–7.51 mm month⁻¹ for runoff, and 33.8–58.7 mm month⁻¹ for evapotranspiration. QDM substantially reduces systematic errors, lowering precipitation RMSE to 23.8–27.8 mm month⁻¹, runoff RMSE to 0.24–1.85 mm month⁻¹, and evapotranspiration RMSE to 3.7–4.3 mm month⁻¹, while preserving projected distributional changes, as confirmed by Kolmogorov-Smirnov (D ≤ 0.072) and Wasserstein (≤ 11.86) metrics. In the second stage, a conditional Generative Adversarial Network (GAN) super-resolves the bias-corrected precipitation fields from ~250 km to 14 km (regridded to 1 km), outperforming bilinear and bicubic interpolation in terms of structural similarity and spatial coherence. The proposed QDM-GAN framework is fully documented and reproducible, with openly available code and data sources, and is intended as a modular post-processing tool that can support downstream modeling applications requiring bias-corrected, high-resolution climate inputs.

General circulation models (GCMs) underpin climate change assessments, yet their coarse spatial resolution and systematic biases constrain their direct use in regional applications. Post-processing approaches such as bias correction and statistical downscaling are therefore widely applied, yet these steps are often implemented independently, leading to inconsistencies between corrected statistics and spatial structure. This study presents a reproducible two-stage framework that integrates Quantile Delta Mapping (QDM) for bias correction with a deep learning-based super-resolution method to improve the statistical fidelity and spatial detail of climate model outputs. The framework is evaluated using precipitation, runoff, and evapotranspiration from three Coupled Model Intercomparison Project Phase 6 (CMIP6) GCMs (Canadian Earth System Model (CanESM5), Hadley Centre Global Environment Model (HadGEM3‐GC31‐LL), and Max Planck Institute Earth System Model (MPI‐ESM1‐2‐HR)), over the Hadejia-Jama'are River Basin in northern Nigeria. We first demonstrate that raw model outputs exhibit substantial biases, with domain-averaged root mean square errors (RMSE) of 48.6–57.3 mm month⁻¹ for precipitation, 0.93–7.51 mm month⁻¹ for runoff, and 33.8–58.7 mm month⁻¹ for evapotranspiration. QDM substantially reduces systematic errors, lowering precipitation RMSE to 23.8–27.8 mm month⁻¹, runoff RMSE to 0.24–1.85 mm month⁻¹, and evapotranspiration RMSE to 3.7–4.3 mm month⁻¹, while preserving projected distributional changes, as confirmed by Kolmogorov-Smirnov (D ≤ 0.072) and Wasserstein (≤ 11.86) metrics. In the second stage, a conditional Generative Adversarial Network (GAN) super-resolves the bias-corrected precipitation fields from ~250 km to 14 km (regridded to 1 km), outperforming bilinear and bicubic interpolation in terms of structural similarity and spatial coherence. The proposed QDM-GAN framework is fully documented and reproducible, with openly available code and data sources, and is intended as a modular post-processing tool that can support downstream modeling applications requiring bias-corrected, high-resolution climate inputs.

The Gravity Recovery and Climate Experiment (GRACE/GRACE-FO) missions provide terrestrial water storage anomalies (TWSA) at coarse spatial resolution (300 km), limiting their application in medium-sized basins. This study develops a machine-learning framework to enhance the spatial interpretability of GRACE mascon TWSA within the 48,000 km² Hadejia-Jama’are River Basin, Nigeria. Hydroclimatic predictors derived from TerraClimate, Global Land Data Assimilation System (GLDAS), and Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) were integrated within a unified 4 km spatial framework. Four machine learning models were evaluated, including Random Forest (RF), Gradient Boosting, Histogram Gradient Boosting, and a Multi-Layer Perceptron. The RF model achieved the highest skill in reproducing mascon-scale TWSA (R² = 0.937; NSE = 0.937; RMSE = 4.36 cm). Aggregation of the 4 km fields back to the mascon scale preserved basin-integrated mass (R² = 0.94), confirming consistency with the original GRACE signal. The resulting groundwater storage anomaly (GWSA) fields resolve sub-basin spatial gradients and seasonal recharge-depletion cycles that are not discernible in the native product. Validation against 31 monitoring wells yielded moderate temporal agreement (Pearson correlation coefficient, r = 0.656), with magnitude discrepancies attributable primarily to scale mismatch and hydrogeological heterogeneity. While not a substitute for in-situ monitoring, the downscaled product enhances basin-scale groundwater assessment in data-scarce semi-arid regions. The framework is transferable to comparable basins and supports regional drought monitoring and water-resource management.

General circulation models (GCMs) underpin climate change assessments, yet their coarse spatial resolution and systematic biases constrain their direct use in regional applications. Post-processing approaches such as bias correction and statistical downscaling are therefore widely applied, yet these steps are often implemented independently, leading to inconsistencies between corrected statistics and spatial structure. This study presents a reproducible two-stage framework that integrates Quantile Delta Mapping (QDM) for bias correction with a deep learning-based super-resolution method to improve the statistical fidelity and spatial detail of climate model outputs. The framework is evaluated using precipitation, runoff, and evapotranspiration from three Coupled Model Intercomparison Project Phase 6 (CMIP6) GCMs (Canadian Earth System Model (CanESM5), Hadley Centre Global Environment Model (HadGEM3‐GC31‐LL), and Max Planck Institute Earth System Model (MPI‐ESM1‐2‐HR)), over the Hadejia-Jama'are River Basin in northern Nigeria. We first demonstrate that raw model outputs exhibit substantial biases, with domain-averaged root mean square errors (RMSE) of 48.6–57.3 mm month⁻¹ for precipitation, 0.93–7.51 mm month⁻¹ for runoff, and 33.8–58.7 mm month⁻¹ for evapotranspiration. QDM substantially reduces systematic errors, lowering precipitation RMSE to 23.8–27.8 mm month⁻¹, runoff RMSE to 0.24–1.85 mm month⁻¹, and evapotranspiration RMSE to 3.7–4.3 mm month⁻¹, while preserving projected distributional changes, as confirmed by Kolmogorov-Smirnov (D ≤ 0.072) and Wasserstein (≤ 11.86) metrics. In the second stage, a conditional Generative Adversarial Network (GAN) super-resolves the bias-corrected precipitation fields from ~250 km to 14 km (regridded to 1 km), outperforming bilinear and bicubic interpolation in terms of structural similarity and spatial coherence. The proposed QDM-GAN framework is fully documented and reproducible, with openly available code and data sources, and is intended as a modular post-processing tool that can support downstream modeling applications requiring bias-corrected, high-resolution climate inputs.