| 18 May 2026
Cross-temporal downscaling and fusion for hourly 0.01° precipitation estimation: A case study in Youxian District, China
Abstract. Reliable precipitation data are essential for fine-scale hydrological applications at the regional level. Consequently, numerous studies have sought to generate high-resolution and high-accuracy precipitation products through spatial downscaling of satellite-based precipitation estimates and bias correction using ground observations. However, few such studies have considered the sub-daily scale, which holds greater application value. In this study, a cross-temporal "downscaling-fusion" framework, termed CTDF, is proposed. Both stages employ extreme gradient boosting (XGBoost) modeling: the first stage spatially downscales daily GPM precipitation from 0.1° to 0.01° using various high-resolution environmental factors, while the second stage fuses the downscaled GPM, cloud properties, and rain gauge observations to generate the final hourly precipitation estimates. With Youxian District, China as the study area, the performance of CTDF was compared against five alternative schemes, and the spatial distribution of the generated precipitation was analyzed. Results indicate that: (1) CTDF exhibits the best overall performance (CC = 0.81, MAE = 0.88 mm, RMSE = 1.95 mm, Bias = 0.4 %), mitigating the systematic underestimation inherent in the original GPM product, while omitting either stage results in performance degradation; (2) CTDF demonstrates more robust performance across different precipitation intensities and diurnal conditions; (3) CTDF substantially enhances the representation of spatial precipitation heterogeneity, increasing the coefficient of variation (CV) of GPM by 170 % and 255 % for convective and stratiform precipitation events, respectively. Overall, the two-stage collaborative design of CTDF achieves spatial refinement and accuracy improvement, providing a viable technical pathway for generating high spatiotemporal resolution precipitation products.
Overall, the manuscript is well organized and the methodology is reasonably designed. The two-stage "downscaling-first, fusion-second" framework offers a degree of novelty in addressing the need for hourly, high-resolution precipitation products. The comparison against five alternative schemes, the spatial visualization results, and the cross-year (temporal generalization) validation together provide reasonably convincing evidence for the effectiveness and robustness of the proposed method. The study has practical value for applications such as regional disaster early warning, and is worthy of recognition. I offer a few questions and suggestions below for the authors' consideration during revision:
1. The Abstract does not mention the results of the independent validation of the proposed method for 2023. It is suggested that the authors add a brief summary of these results.
2. In Section 2.2.4 (Ground observation data), the quality control workflow for the rain gauges is summarized only as "outlier detection, spatial consistency check, and temporal consistency check." It is suggested that the authors provide some specific methodological description of these three steps, with appropriate references where available.
3. The temporal disaggregation in Eqs. (3)–(4), and its accompanying description, require further clarification. The source method cited by the authors, Ma et al. (2022), is computed on a per-0.1° grid basis—each grid cell uses the ratio of its own hourly value to its own daily value to obtain the allocation weight, so that the intra-day variation of each cell is preserved separately. In Eq. (4), however, both the numerator and denominator of R(h) are described as "summing all 0.1° grids in the study area," which yields a single value of R(h) per time step that is then applied to all 0.01° pixels. This is not fully consistent with the cited method. It is unclear how the authors actually implemented this step: if the calculation is in fact performed per grid cell, then Eq. (4) is not written accurately and should be revised; if a single study-area-wide ratio was indeed used, please justify this treatment and provide an appropriate supporting reference.
4. The GMWWDA method proposed by Ma et al. (2020) is used as one of the benchmark schemes in this study. In that original paper, the authors derived and compared GPM downscaling results based on several different cloud properties; however, the present manuscript does not specify which cloud property was used for the downscaling result adopted here. It is suggested that the authors briefly clarify this.
5. In Section 3.3.2 (Accuracy assessment across different precipitation intensity classes), the precipitation is divided into three intensity categories (Light, Moderate-to-heavy, and Rainstorm and above) for the inter-scheme comparison, but it is not explained how the thresholds for this classification were determined, which somewhat undermines the rigor. The authors are advised to clarify this, and to indicate the source (e.g., a relevant reference or standard) if such a basis exists.
6. There are several minor errors in the text, for example the incorrect numbering of Table 1 and the incorrect caption of Table 5. The authors are advised to check the manuscript carefully and correct these issues.