Search Results (995)

The accurate estimation of precipitation is important for hydro-meteorological applications. The evaluation of Gridded Precipitation Products (GPPs) must account for the zero-inflated nature of precipitation data and differences in spatial support between rain gauges and satellite grids. Traditional validation approaches in East Africa evaluate datasets in a temporally rigid manner, penalising them for minor spatial or timing displacements. This study evaluates the phase alignment, event detection, and precipitation volume of ERA5, IMERG, CHIRPS, and TAMSAT against the Trans-African Hydro-Meteorological Observatory (TAHMO) network across East Africa. First, we apply variance-stabilising transformations to address skewness and zero inflation in daily precipitation. Second, we utilise consecutive overlapping temporal windows and temporal neighbourhood verification to isolate timing accuracy from volume. Finally, we cluster raw diurnal precipitation cycles into regimes. Our results reveal that temporal phase misalignments are localised. At the diurnal scale, the GPPs and TAHMO assign different regimes at 58.1% of the stations. At the daily scale, IMERG exhibits superior phase alignment and event detection, whereas CHIRPS and TAMSAT preserve precipitation volume more accurately. Full article

Accurate correction of daily satellite-derived precipitation estimates in data-scarce tropical regions remains a critical challenge for climate monitoring, agriculture, and public health. Satellite products such as CHIRPS offer broad spatial coverage but exhibit systematic biases relative to ground-based observations particularly in complex terrain under bimodal tropical regimes influenced by ENSO. We propose a Functional Generalised Additive Mixed Model (FGAMM) that corrects CHIRPS-derived precipitation estimates by treating the annual accumulated precipitation curve as a functional response and the satellite accumulation curve as a functional covariate, while incorporating station-level random effects and the Southern Oscillation Index. This functional formulation targets the systematic, slowly varying bias between satellite and ground-station accumulation, the quantity most relevant for water-balance applications such as reservoir management and agricultural planning rather than day-to-day storm nowcasting. Applied to 62 IDEAM stations in the Valle del Cauca department of Colombia (2012–2020), the FGAMM achieves a mean cross-validation RMSE of 0.68 mm/day (95% bootstrap CI: 0.61–0.75), a substantially lower error than linear regression, SVM, and Random Forest within this dataset, where the gap is statistically significant across all competing methods. This magnitude of advantage is not reproduced when applying the same fitting-and-differencing pipeline, via a simplified concurrent approximation, to an independent national-network dataset; we discuss the methodological factors that likely contribute to this discrepancy—including an inherent smoothness asymmetry between the penalised-spline FGAMM fit and the unconstrained benchmark models, and differences in validation design between the two checks—in the Discussion, and treat the true size of the FGAMM’s advantage as an open question pending a fully controlled comparison. Corrected estimates are currently restricted to the calibrated station locations; because CHIRPS provides near-global daily coverage from 1981 to the present, we discuss how the same modelling approach could in principle be applied to other tropical or subtropical regions with a sparse reference station network, including areas of Latin America, sub-Saharan Africa, and South Asia where station density is similarly limited. Full article

Tropical cyclone (TC) intensity estimation over the western North Pacific remains affected by uncertainties in satellite observations, best-track records, and rapidly evolving inner-core structures. To further exploit information on cloud-system vertical structure and its temporal evolution, this study introduces FY-4A/B cloud-top height (CTH) products and develops CTH-TCNet, a three-branch gated-fusion model that integrates infrared brightness temperature, CMORPH precipitation, and multidimensional CTH information for TC intensity estimation. The model consists of a CNN-based spatial branch, an LSTM-based temporal branch representing CTH evolution over the preceding 12 h, and a shortcut branch preserving current-time CTH statistics. Systematic ablation experiments show that the contribution of CTH is closely related to its representation and fusion strategy. Directly adding a single-time-step two-dimensional CTH field as an additional spatial channel provides no further performance gain, whereas historical CTH evolution and current-time CTH statistics provide complementary information. Jointly representing these two types of information through the temporal and shortcut branches yields the best performance. The final model achieves an MAE of 6.26 kt and an RMSE of 7.41 kt on the test sets. Intensity-stratified results further show that CTH generally provides larger improvements for TY, STY, and Super TY than for TS. Interpretability analyses indicate that, as TC intensity increases, the model exhibits greater reliance on CTH temporal evolution and structural information from the inner-core and eyewall-adjacent regions, with these dependence patterns being broadly consistent with known characteristics of TC inner-core convective organization and eyewall-related structures. These results indicate that FY-4A/B CTH provides valuable complementary structural and temporal information for satellite-based TC intensity estimation. Full article

The use of remote sensing data in hydrological applications has increased, especially in regions with limited ground-based observations. Satellite precipitation products (SPPs) provide extensive temporal and spatial coverage but may contain biases that can affect their performance in hydrological simulations. This study evaluates the performance of four near-real-time SPPs for daily streamflow modeling in the Tulijá River Basin (TRB), Mexico: Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN)-Cloud Classification System (CCS), PERSIANN-Dynamic Infrared Rain Rate near real-time (PDIR-Now), and the Early Run and Late Run products of the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) (IMERG). The SPPs were first compared with meteorological station precipitation data and subsequently bias-corrected using the Linear Scaling (LS) method. The CEQUEAU hydrological model simulated streamflow using three precipitation datasets: meteorological stations, original SPPs, and bias-corrected SPPs. For simulations using observed precipitation, the model was calibrated for 1991–2014 and validated for 1968–1990; for SPP-based simulations, calibration and validation were performed for 2003–2011 and 2012–2014, respectively. Model performance was assessed using the Nash–Sutcliffe efficiency (NSE), percent bias (PBIAS), and coefficient of determination (R²). The results show that CEQUEAU performance varies by precipitation dataset. Simulations using observed precipitation yielded NSE values close to 0.70 during both calibration and validation, whereas the original SPPs yielded NSE values below 0.18, including negative values. After bias correction, IMERG-Early and IMERG-Late yielded NSE values of approximately 0.55 during both periods. These findings highlight the importance of analyzing the performance of near-real-time SPPs in hydrological applications, especially in tropical regions with complex topography. Full article

Accurate daily gridded precipitation estimates are needed to characterize spatial rainfall variability and support retrospective hydrological analysis in Southeast Asia, where complex rainfall regimes and sparse rain gauge coverage remain major challenges. This study develops an Extended Triple Collocation Analysis (ETCA)-based merged daily precipitation dataset for Southeast Asia using 15 multi-source gridded precipitation products for 2000–2014. The products include gauge-based, satellite-based, reanalysis-based, and merged datasets, all regridded to a common 10 km × 10 km grid. ETCA was applied to all 455 possible three-product combinations to estimate product-level reliability, expressed as the squared correlation coefficient and error variance at each grid cell. The results reveal substantial spatial variability in product reliability. The final ETCA-merged product was generated through pixel-wise reliability-based product selection, quantile-based distributional adjustment using the locally highest-ranked product as an internal reference, and equal-weight averaging of the selected adjusted products. Validation against GSOD daily observations shows that the ETCA-merged product achieves lower RMSD of 2.95 mm day⁻¹, compared with 2.96 mm day⁻¹ for the simple all-product ensemble and 3.01 mm day⁻¹ for the ensemble of the five most regionally reliable products. The corresponding temporal correlations are 0.79, 0.81, and 0.78, respectively. The merged product also improves the representation of high-percentile rainfall, although very intense rainfall remains underestimated. Spatial climatology and annual cycle analyses indicate that the ETCA-merged product preserves the main rainfall patterns and seasonal evolution of Southeast Asia while introducing local adjustments based on product reliability. These findings demonstrate that ETCA provides a useful framework for developing uncertainty-informed precipitation datasets in regions with sparse gauge observations and spatially heterogeneous product performance. Full article

Satellite precipitation products like GPM IMERG are crucial for hydrological modeling and disaster prevention; yet, their reliability in complex mountainous monsoon regions remains challenging. While the latest IMERG V07 introduces key upgrades, including a Climatological Calibration Algorithm (CCA), its cross-scale error propagation mechanisms and performance heterogeneity in complex underlying surfaces are poorly understood. This study evaluates the daily and monthly performance of IMERG V07 and V06 (Early, Late, and Final Runs) from 2014 to 2020 against 91 rain gauges in Guangxi, China—a typical mountainous monsoon region. The evaluation employs multiple statistical metrics and a multi-dimensional stratification approach based on elevation, precipitation intensity, and seasonality to quantify error propagation and climate-topography coupling effects. The results reveal that V07, particularly the Late Run, enhances daily precipitation detection capabilities, it significantly increases the proportion of systematic positive bias from 62.3 to 64.8% (V06) to 67.2–68.9% (V07). Consequently, upon temporal aggregation to the monthly scale, this systematic overestimation is severely amplified, leading to degraded performance, with the Final Run suffering the most substantial accuracy loss. Furthermore, retrieval accuracy is heavily constrained by surface heterogeneity, with systematic overestimation surging in areas where relatively dry (mean annual precipitation < 1300 mm) and complex terrain (elevation 100–500 m) coincide. The introduced CCA effectively improved dry season estimations but failed during wet season by introducing substantial positive biases. Ultimately, while V07 better captures short-term precipitation dynamics, its structural systematic biases compromise long-term cumulative reliability, highlighting the necessity for physics-based bias correction in hydrological applications and dynamic calibration in future algorithm upgrades. Full article

Freshwater scarcity can weaken water governance when hydrological pressure interacts with intensive agricultural demand, regulatory weakness, and political conflict. This research evaluates whether Irrigation District 005 (IR 005) in Chihuahua, northern Mexico, demonstrates local water-governance fragility across three domains: public security, the rule of law, and the ability to sustain water access and food production. A mixed-methods approach integrates legal and human rights documentation, institutional records, published studies, and a structured media review with hydrological, agricultural, climatic, and reservoir data. Water balances were analysed for 1998–2023, precipitation trends for 1980–2020, and crop water requirements were estimated using the Food and Agriculture Organization’s Irrigation and Drainage Paper No. 56 (FAO-56) Penman–Monteith framework, the crop coefficient (Kc), the water-stress coefficient (Ks), the United States Soil Conservation Service (SCS) Curve Number method, and application-efficiency assumptions. The 2020 water conflict resulted in fatalities, injuries, arrests, and documented human rights violations. Rule-of-law capacity was further diminished by unauthorised withdrawals, cultivation beyond authorised irrigation plans, and limited enforcement. The annual water balance shifted to persistent deficits after 2016, reaching an estimated deficit of 2268 cubic hectometres (hm³) in 2020. Annual precipitation did not exhibit a statistically significant monotonic decline during 1980–2020 (Mann–Kendall Z = −0.79, τ = −0.0878, p = 0.4251; Sen’s slope = −1.1628 mm yr⁻¹; Mann–Whitney p = 0.5313), indicating that recent stress is more closely linked to production scale, crop mix, governance conditions, and irrigation efficiency than to a long-term reduction in rainfall. Sensitivity analysis revealed that ±15% changes in Kc and Ks altered gross water requirements by approximately ±16–17%, while equivalent changes in effective precipitation produced changes of only 1–3%. These results demonstrate heightened water-governance fragility resulting from mutually reinforcing hydrological, institutional, and conflict-related pressures. Future research should refine locally calibrated water-demand parameters and develop reproducible monitoring systems that combine hydrological, institutional, satellite, and participatory data to support anticipatory, transparent, and rights-based water governance. Full article

Remote sensing of clouds and precipitation is undergoing a significant transition from descriptive observations toward process-oriented diagnoses. This transition has not progressed linearly, but has gradually taken place alongside the rapidly developing multi-source observation capabilities over recent decades. In terms of clouds and precipitation observational platforms, satellites provide continuous global-scale monitoring, airborne platforms complement high-resolution sampling of key processes, and ground-based observations offer long-term vertical structure evolution, which form an integrated space–air–ground observation system. In terms of clouds and precipitation retrieval algorithms, active–passive combination remote sensing significantly improves the ability to retrieve macro- and microphysical characteristics and structures, and machine learning methods further expand parameter estimation capabilities in complex scenarios. Nevertheless, key bottlenecks still persist in retrieval non-uniqueness, sensor trade-offs, cross-platform calibration, and validation over oceans, mountains, and polar regions. Based on the above background, this paper provides a systematic review of recent progress in clouds and precipitation physics remote sensing, focusing on the development of multi-platform collaborative observations, the evolution of microphysical parameter retrieval methods, and the improvements in remote sensing characterization of cloud and precipitation formation mechanisms. It further points out that future development will increasingly rely on improved uncertainty quantification, incorporation of physical constraints into retrieval frameworks, and the establishment of standardized multi-source datasets. Full article

Water security is a significant challenge for the Saudi Arabia Kingdom’s development and stability, affecting other economic sectors beyond the water sector. Insufficient water resources are causing economic and social crises; addressing this issue is crucial for the country’s growth and stability. This study aims to manage water resources in central Saudi Arabia (Wadi AS SAHBAA) through a two-level approach. The first level involves extracting rainfall amounts from satellite imagery to predict future rainfall intensity using PERSIANN-CCS-CDR data. The second level focuses on monitoring groundwater recharge using geographic information systems (GIS) and remote sensing techniques. The study further seeks to understand rainstorm behavior influenced by climate variability and applies geomatics techniques for quantitative analysis. The results show that the Wadi AS SAHBAA basin receives an annual precipitation of 116.6 mm/year and mean annual precipitation of 9.7 mm. The year 2019 experienced the highest recorded precipitation, reaching 222.3 mm and mean annual precipitation of 18.5 mm. Between 2013 and 2019, the AS SAHBAA region experienced increased rainfall driven by intense storm events; however, it declined during the period 2020–2022. Moreover, 2021 recorded the lowest annual precipitation of 53.8 mm with mean annual precipitation of (4.5 mm), possibly linked to reduced storm activity due to the COVID-19 pandemic. The study uses several methods to estimate groundwater recharge from rainfall and concludes that the average infiltration during 2013–2022 was varying from 1.59–36.63 mm, representing about between 1.03 and 28.5% of total rainfall. This is reflected in groundwater storage capacity, which ranges from approximately 1.65 to 38.27 million m³ yearly. This study provides a framework for monitoring precipitation and groundwater recharge and offers practical recommendations for regional development and sustainable water management. Full article

HY–4A is China’s first ocean salinity remote-sensing satellite, launched into orbit in November 2024 and currently in operational service. The LASMR (L-Band Aperture Synthesis Microwave Radiometer) is the L-band synthetic aperture radiometer onboard the HY–4A satellite. This study validates the LASMR Level-2 SSS (sea surface salinity) product using in situ salinity observations from Argo floats, covering the period from November 2024 to December 2025. Global analysis indicates that the LASMR SSS retrieval uncertainties show a distinct zonal distribution, which primarily reflects the impact of sea surface temperature (SST) and sea surface wind speed on SSS retrieval accuracy. A lower SST reduces the sensitivity of brightness temperature (TB) to SSS variations, and a high wind speed degrades the sea surface roughness correction. Both factors lead to increasing uncertainties in SSS retrieval. Furthermore, atmospheric parameters including water vapor content and precipitation also affect the SSS retrieval uncertainty. The influence of water vapor may originate from its coupling with SST/wind speed and inherent uncertainties in the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis data. The effect of precipitation is more complex: it increases ocean TB through rain-induced surface freshening and additional rain-induced roughening, which aliases into the satellite signal. Moreover, precipitation-enhanced vertical salinity gradients amplify the vertical representativeness error arising from the depth difference between satellite sensing and Argo measurements. Meanwhile, impacted by land brightness temperature contamination and radio-frequency interference (RFI), the SSS retrieval accuracy of HY–4A decreases significantly in coastal waters compared with the open ocean. Since the traditional buoy–satellite dual-matching method tends to overestimate uncertainties in satellite data, an Argo/HY–4A/SMAP (Soil Moisture Active Passive) triple-collocation dataset is used to estimate the LASMR SSS retrieval uncertainties. The triple-collocation method yields robust uncertainty estimates for both satellites (HY–4A and SMAP) over the global ocean and high-salinity-variability regions. In conclusion, the global uncertainty of the HY–4A LASMR SSS product is 0.35 psu. These results provide a reference for future product refinement and improvements in HY–4A SSS retrieval algorithms. Full article

How modeled ecosystem carbon balance varies across the contrasting hydroclimatic zones of the Yellow River Basin, and whether its relationships with climate extremes differ among these zones, remain insufficiently understood. To address this knowledge gap, this study examined the spatiotemporal variability of modeled net ecosystem productivity (NEP) and its associations with temperature- and precipitation-extreme indices across three major ecological zones of the basin. Using satellite-derived NDVI, reanalysis climate data, and extreme climate indices for 2000–2022, we estimated NEP from CASA-simulated net primary productivity and modeled heterotrophic respiration, and assessed its temporal trends and climatic associations across three ecological zones. Three principal findings were obtained: (1) Over the 23-year study period, basin-wide NEP showed a statistically significant increasing tendency (β = 2.699 g C m⁻² a⁻¹), with a well-defined spatial gradient characterized by relatively lower productivity in the northwestern areas and elevated productivity toward the southeastern regions. (2) Modeled NEP increased across all four seasons. Summer showed the largest positive trend, whereas autumn and winter remained net carbon-release seasons but became progressively less negative over the study period. (3) The statistical associations between modeled NEP and extreme climate indices varied markedly across ecological zones. Extreme-precipitation indices were generally positively associated with modeled NEP in the arid northwestern zone, whereas several extreme-temperature indices showed predominantly negative associations in the northeastern monsoon-influenced region. Collectively, these findings provide model-based evidence of spatially differentiated associations between ecosystem carbon balance and climate extremes across the YRB, supporting regional ecosystem assessment and climate-adaptation planning. Full article

Understanding the spatial structure of tree communities is fundamental for evaluating ecological interactions and management dynamics in agroforestry systems. However, the structural complexity and small spatial scale of traditional agroecosystems often limit the use of conventional remote sensing approaches. Recent advances in drone-based photogrammetry offer new opportunities to reconstruct the three-dimensional structure of vegetation at high spatial resolution and to quantify tree-level structural attributes. In this study, we applied aerial photogrammetry from unmanned aerial vehicles (UAVs) to characterize the spatial structure of agroforestry systems in traditional home gardens (THGs) in the Yucatan Peninsula, Mexico. The immediate neighborhood structure of the tree community of 20 THGs distributed along a south–north precipitation gradient was analyzed using two focal species as anchor references: Spondias purpurea and Annona muricata. High-resolution orthomosaics and three-dimensional point cloud models were generated to estimate structural attributes, including tree height, crown area, crown surface area, and canopy volume, which were combined with field measurements of diameter at breast height. Spatial indices describing aggregation, dominance, and neighborhood diversity were calculated to evaluate tree spatial organization and potential interaction patterns. The UAV-derived structural metrics revealed significant differences in canopy architecture across regions and between focal species. Regardless of the focal species, trees in the southern region exhibited greater height, crown diameter, and canopy volume than those in the northern region. Moreover, the spatial arrangement of tree communities also differed depending on which focal species was considered as the anchor, suggesting contrasting strategies of canopy dominance and spatial coexistence. Finally, our results validate the use of drone-based photogrammetry as an effective approach for capturing fine-scale spatial structure in complex agroforestry systems. By enabling detailed three-dimensional reconstruction of tree canopies, UAV remote sensing offers an affordable, simple approach to investigate neighborhood interactions, management effects, and structural dynamics in traditional agroecosystems that are difficult to assess using conventional field- or satellite-based methods. Full article

This study investigates the spatiotemporal dynamics of Land Use/Land Cover (LULC) along precipitation gradients across Türkiye by integrating high-resolution satellite-based precipitation datasets (CHIRPS, TerraClimate, and TRMM) with the European Space Agency (ESA) WorldCover (10 m) product and multi-sensor Normalized Difference Vegetation Index (NDVI) composites (Landsat, MODIS, Sentinel-2). Türkiye’s heterogeneous climate, characterized by a sharp contrast between humid coastal belts and semi-arid interiors, serves as a natural laboratory to assess ecosystem responses to moisture availability. The results reveal a systematic and non-linear transformation of LULC classes as precipitation increases. In low-rainfall zones (200–400 mm), agricultural activities and bare surfaces predominate, reflecting human-induced land management in water-constrained environments. A critical ecological threshold was identified between 400 mm and 700 mm, where grassland areas expand rapidly, becoming the dominant class. Beyond the 900 mm isohyet, forest cover exhibits a sharp increase, approaching nearly 100% dominance in regions exceeding 1200 mm, effectively displacing other LULC categories. Comparative analysis of precipitation products shows that while all datasets capture the “coastal-wet/inland-dry” pattern, TRMM tends to overestimate winter precipitation (exceeding 100 mm), whereas CHIRPS and TerraClimate provide more conservative estimates (75–80 mm). Overlay analyses between seasonal NDVI and precipitation confirm a pronounced “time-lag effect” in vegetation phenology. Despite peak precipitation occurring in winter (~75 mm), NDVI reaches its minimum (~0.03) due to thermal limitations and dormancy. Conversely, vegetation greenness peaks during the dry summer months (NDVI ~0.14 to 0.40), utilizing antecedent soil moisture stored during the spring recharge. High-resolution Sentinel-2 data proved superior in delineating micro-topographic vegetation responses compared to Landsat and MODIS. These findings provide a scientific baseline for sustainable land management and climate adaptation strategies, highlighting that precipitation thresholds are the primary determinants of Türkiye’s ecological boundaries. Full article

Mediterranean coastal regions are increasingly affected by hydrometeorological hazards associated with high-impact weather events, including cyclones. Between 18 and 21 January 2026, the intense extratropical cyclone Harry affected Sicily, Sardinia, and Calabria, producing severe weather conditions including heavy precipitation, strong winds, and extreme wave activity. This study investigates both the meteo-marine characteristics of the event and its associated damage in Calabria, where the cyclone triggered multiple hazards (wave storms, landslides, flooding, and strong winds). Meteo-marine forcing was characterized using integrated rainfall data, wave parameters, and wind data. In situ observations, radar-derived precipitation estimates, satellite measurements, and model-based reanalysis products were combined to provide a comprehensive evaluation of the event. A georeferenced database of 195 damage records was compiled and classified according to the EU Floods Directive (2007/60/EC), allowing spatial analyses within a GIS framework. Although the cyclone produced exceptional rainfall totals, locally exceeding 580 mm in 90 h, the distribution of impacts reveals the predominance of coastal processes. Wave storm-related damage accounted for 68% of all recorded impacts, mainly affecting transportation and communication infrastructures, tourism facilities, and population. The prevalence of coastal damage appears to be linked not only to the intensity of marine forcing but also to its persistence which locally exceeded the maximum climatological persistence, suggesting that event duration plays a critical role in determining impact severity. Geomorphological analyses indicate that short-term coastal vulnerability is influenced not only by long-term shoreline evolution but also by local topographic characteristics and exposure to marine forcing. These findings contribute to improving risk assessment and mitigation strategies for Mediterranean coastal regions under a changing climate. Full article

High-resolution precipitation fields are essential for flash-flood forecasting and hydrological risk management, especially in small and medium-sized basins, yet single-source precipitation products often show limited accuracy over complex terrain. This study develops a two-stage machine-learning framework for 1 km/1 h multi-source precipitation fusion over Shaoxing, China, during the 2025 flood season. In the first stage, a machine-learning classifier identifies precipitation occurrence and reduces zero-inflated noise; in the second stage, an optimized tree-based residual-regression model corrects precipitation estimates for rainy samples. A 61-dimensional feature set was constructed by integrating satellite precipitation estimates, weather-radar precipitation estimates from the Zhejiang radar network, temporal-lag and accumulation statistics, neighborhood descriptors, cyclic time variables, and terrain-derived interaction features, with gauge observations used as the training target. After quality control, the dataset comprised 41,458 hourly station samples from 72 rain gauges. The stations were divided at the station level into a 57-station development set and a fixed 15-station held-out spatial test set containing 8637 hourly samples. Station-blocked fivefold cross-validation within the development set was used for model selection, hyperparameter tuning, and probability-threshold selection, whereas the held-out stations were used only for final performance evaluation. On the fixed held-out test set, the occurrence classifier achieved an overall accuracy of 0.947, with a probability of detection of 0.806, a false alarm ratio of 0.158, a critical success index of 0.700, and an F1 score of 0.823. For quantitative estimation, the two-stage fusion product reduced root mean square error from 2.342 mm for satellite precipitation estimates to 1.189 mm, corresponding to a 49.22% reduction, and decreased mean absolute error from 0.712 mm to 0.262 mm, while increasing the coefficient of determination to 0.685. The fused precipitation product also improved the detection of intense rainfall events, with probability of detection and critical success index reaching 0.511 and 0.442, respectively, for events exceeding 10 mm/h, while reducing false weak precipitation and showing closer agreement with observed station-level spatial variability. By separating precipitation-occurrence identification from rainfall-intensity correction, the framework reduces zero-inflated bias, improves heavy-rainfall representation, and demonstrates predictive skill at gauges excluded from model development during the 2025 flood season. Full article