Abstract
<jats:p>Abstract. Global monthly streamflow observations are fundamental for understanding changes in the water cycle, supporting large-sample hydrology, and informing water-resources assessments. However, currently available open station archives still suffer from substantial limitations in temporal continuity and spatial coverage. Here we present GSIM-PLUS, a gap-filled global monthly streamflow dataset for 1995–2015 designed to improve the completeness and reusability of global runoff records. Using the GSIM monthly archive as the basis, we identified 7,323 high-completeness anchor stations and 8,731 target stations from 30,959 gauges. Basin descriptors from five groups – climate, topography, soil, spatial location, and hydrology – were used to identify the most similar donor stations for each target site. Donor-Trend Recursive Regression (DTRR) was adopted as the default imputation method, with a guarded fallback to baseline MAML for a limited subset of very-low-flow stations in order to improve production stability under long recursive gaps. Multi-scenario validation shows that DTRR achieved the best overall performance under random 30 % masking (NSE = 0.865; KGE = 0.920) and remained robust for both 12-month continuous gaps (NSE = 0.795) and very long gaps exceeding 25 months (NSE = 0.511). Independent validation using 16 GRDC stations across six regions further confirmed good transferability, while indicating that temporal agreement was generally more robust than exact magnitude reconstruction under donor-limited or long-gap conditions. Under the guarded DTRR production scheme, GSIM-PLUS fills 303,271 missing monthly records for 16,054 stations, increasing the median completeness of target stations from 66.3 % to 81.0 % and that of the full dataset from 86.9 % to 95.2 %. Each released record is accompanied by quality and context metadata, including reconstruction class, gap length, fill method, and basin-context flags. GSIM-PLUS provides a more continuous and traceable global monthly streamflow resource for regional hydrological analysis, large-sample studies, model evaluation, and related monthly-scale applications. The GSIM-PLUS dataset is publicly available through Zenodo at https://doi.org/10.5281/zenodo.21425702.</jats:p>