Many modeled and observed data are in coarse resolution, which are required to be downscaled. This study develops a probabilistic method to downscale 3-hourly runoff to hourly resolution. Hourly data recorded at the Poldokhtar Stream gauge (Karkheh River basin, Iran) during flood events (2009–2019) are divided into two groups including calibration and validation. Statistical tests including Chi-Square and Kolmogorov–Smirnov test indicate that the Burr distribution is proper distribution functions for rising and falling limbs of the floods’ hydrograph in calibration (2009–2013). A conditional ascending/descending random sampling from the constructed distributions on rising/falling limb is applied to produce hourly runoff. The hourly-downscaled runoff is rescaled based on observation to adjust mean three-hourly data. To evaluate the efficiency of the developed method, statistical measures including root mean square error, Nash–Sutcliffe efficiency, Kolmogorov-Smirnov, and correlation are used to assess the performance of the downscaling method not only in calibration but also in validation (2014–2019). Results show that the hourly downscaled runoff is in close agreement with observations in both calibration and validation periods. In addition, cumulative distribution functions of the downscaled runoff closely follow the observed ones in rising and falling limb in two periods. Although the performance of many statistical downscaling methods decreases in extreme values, the developed model performs well at different quantiles (less and more frequent values). This developed method that can properly downscale other hydroclimatological variables at any time and location is useful to provide high-resolution inputs to drive other models. Furthermore, high-resolution data are required for valid and reliable analysis, risk assessment, and management plans.

许多建模和观测数据的分辨率较粗，需要按比例缩小。本研究开发了一种概率方法，将 3 小时径流缩减为小时分辨率。在洪水事件（2009-2019 年）期间，Poldokhtar 流测量仪（伊朗卡克赫河流域）记录的每小时数据分为校准和验证两组。包括卡方检验和Kolmogorov-Smirnov 检验在内的统计检验表明，Burr 分布是校准（2009-2013 年）洪水过程线上升和下降分支的适当分布函数。应用从上升/下降分支上构建的分布中的条件上升/下降随机抽样来产生每小时径流。每小时缩小的径流根据观察重新调整以调整平均三小时数据。为了评估所开发方法的效率，使用包括均方根误差、Nash-Sutcliffe 效率、Kolmogorov-Smirnov 和相关性在内的统计测量方法来评估缩减方法在校准和验证方面的性能（2014-2019 ）。结果表明，每小时缩小的径流与校准和验证期间的观测结果非常一致。此外，降尺度径流的累积分布函数与观测到的两个时期上升和下降翼的累积分布函数密切相关。尽管许多统计降尺度方法的性能在极值上有所下降，但开发的模型在不同的分位数（越来越少的值）上表现良好。这种开发的方法可以在任何时间和地点适当地缩减其他水文气候变量，这对于提供高分辨率输入来驱动其他模型很有用。此外，有效和可靠的分析、风险评估和管理计划需要高分辨率数据。