Abstract
Global climate change is intensifying the hydrological cycle, manifested through an increased frequency of extreme precipitation events that pose substantial threats to water security and ecosystem resilience. Precipitation concentration indicators are critical for diagnosing these changes, yet their application has been constrained by data limitations: a reliance on fragmented station observations and a critical disconnect between historical benchmarks and future projections. To bridge this gap, we present the multi-precipitation concentration indicators dataset (MPCID) for mainland China, a spatiotemporally continuous resource spanning 1961–2100. MPCID integrates historical in-situ and gridded observations (1961–2022) with high-resolution (0.25°), statistically downscaled CMIP6 projections across four SSP scenarios (2015–2100). The dataset incorporates four key indicators: the precipitation concentration degree (PCD), precipitation concentration period (PCP), daily precipitation concentration index (DPCI), and monthly precipitation concentration index (MPCI). Rigorous validation against station data established PCD as the most reliable indicator, characterized by minimal errors, near-optimal correlation, and negligible bias across both historical and future scenarios. While DPCI exhibited moderate error control, its limited daily-scale correlation points to inherent stochasticity in short-term precipitation. MPCI demonstrated reduced sensitivity to extreme precipitation events, whereas PCP showed systematic limitations in temporal phase alignment despite retaining pattern recognition capability. By integrating historical fidelity with future scenarios, MPCID overcomes prior data fragmentation and establishes an indispensable foundation for investigating precipitation dynamics, assessing climate impacts on hydrology and agriculture, and informing adaptive management strategies.
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Data availability
The dataset, including detailed methodological descriptions and additional validation results, are available with the main paper.
Code availability
Custom code used for statistical downscaling and precipitation concentration indicator calculation are deposited in a permanent repository. The repository is Zenodo, and the DOI for this Zenodo repository is https://doi.org/10.5281/zenodo.17503458. The code is released under the Creative Commons Attribution 4.0 International License (CC BY 4.0). This license permits unrestricted reuse of the code, including commercial use, on the condition that appropriate attribution is provided.
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Acknowledgements
This study was supported by the National Natural Science Foundation of China (42261026, 41761014), the Open Fund of Xinjiang Arid Zone Water Cycle and Water Utilization Laboratory (XJYS0907-2023-01), and the “Light of the West” Program for Young Scholars, Chinese Academy of Sciences (25JR6KA005).
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D.Y.Z. developed the methodology, wrote the original draft, organized the data, performed visualization, conducted validation, and conceptualized the study. X.M.L. reviewed and edited the manuscript, acquired funding, and contributed to conceptualization. L.H.L. and G.G.W. reviewed and edited the manuscript. Y.L.T. developed code components and methodology. C.M.Y. developed the codes and methods. H.E.D. contributed to visualization, validation, and methodology. X.X.J. implemented software tools and performed formal analysis. All authors have made substantial contributions to this research.
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Zhang, D., Li, X., Li, L. et al. MPCID, A new high-resolution multi-precipitation concentration indicators dataset for mainland China. Sci Data (2026). https://doi.org/10.1038/s41597-025-06515-2
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DOI: https://doi.org/10.1038/s41597-025-06515-2
