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Global Climate Models (GCMs) precipitation scenarios are often characterizedby biases and coarse resolution that limit their direct application forbasin level hydrological modeling. Bias-correction and spatialdisaggregation methods are employed to improve the quality of ECHAM4/OPYCSRES A2 and B2 precipitation for the Ping River Basin in Thailand.Bias-correction method, based on gamma-gamma transformation, is applied toimprove the frequency and amount of raw GCM precipitation at the grid nodes.Spatial disaggregation model parameters (β,σ<sup>2</sup>),based on multiplicative random cascade theory, are estimated usingMandelbrot-Kahane-Peyriere (MKP) function at <i>q</i>=1 for each month.Bias-correction method exhibits ability of reducing biases from thefrequency and amount when compared with the computed frequency and amount atgrid nodes based on spatially interpolated observed rainfall data. Spatialdisaggregation model satisfactorily reproduces the observed trend andvariation of average rainfall amount except during heavy rainfall eventswith certain degree of spatial and temporal variations. Finally, thehydrologic model, HEC-HMS, is applied to simulate the observed runoff forupper Ping River Basin based on the modified GCM precipitation scenarios andthe raw GCM precipitation. Precipitation scenario developed withbias-correction and disaggregation provides an improved reproduction ofbasin level runoff observations

Spatial disaggregation of bias-corrected GCM precipitation for improved hydrologic simulation: Ping River Basin, Thailand