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This study presents some results about biascorrectionseasonal rainfall forecast from the regional spectral model (RSM), following two methods are quantile-quantile with an approximate gamma function (QM-G), and Bayesian joint probability (BJP). RSM ran forecast for the period 1982-2014, with data input from global model CFS,and lead time up to five months. The results show that the BJP made the correlationbetween rainfall forecastand observation increased significantly, the coefficient correlation after corrected is about 0.77 in all three lead times. The bias and error after did correctly by BJP were reduced away clearly, the differencesare almost notin all of three lead times, the error in months from April to October is the smallest and about 20-50%, therein the Northwest climate gives the smallest error. The correction with QM-G did not improve the correlation and bias, which is also made the model losing systematic ofthe error. Keywords: Rain correction, seasonal forecast, Vietnam region, RSM

This study evaluates the estimated rainfall from Nha Be radar observation according to different experimental formulas. The results show that in six estimation formulas used, the formulation of Joss's large-scale rainfall estimation and Marshall-Plamer's formulation can be used to estimate the precipitation for the single southwest monsoon with an error about 5 mm/h and usually less than the observed value. The rainfall estimation formulas by Nguyen Huong Dien is the optimum estimation for the 9 synoptic rainfall patterns: Intertropical Convergence Zone, Southwest monsoon associated with Intertropical Convergence Zone, low-pressure area, thunderstorm, disturbance in East winds, low-pressure area associated with Southwest monsoon, low-pressure troughs have a Northwestern-Southeasterly direction and other synoptic patterns with errors: 5mm/h, 9.5 mm/h, 14 mm/h, 7mm/h, 19 mm/h, 8 mm/h, 8.5 mm/h, 9.5 mm/h and 14.7 mm/h, respectively. Keywords: Radar, rain estimate.

To assist the government of Vietnam in its efforts to better understand the impacts of climate change and prioritise its adaptation measures, dynamically downscaled climate change projections were produced across Vietnam. Two Regional Climate Models (RCMs) were used: CSIRO’s variable-resolution Conformal-Cubic Atmospheric Model (CCAM) and the limited-area model Regional Climate Model system version 4.2 (RegCM4.2). First, global CCAM simulations were completed using bias- and variance-corrected sea surface temperatures as well as sea ice concentrations from six Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models. This approach is different from other downscaling approaches as it does not use any atmospheric fields from the GCMs. The global CCAM simulations were then further downscaled to 10 km using CCAM and to 20 km using RegCM4.2. Evaluations of temperature and precipitation for the current climate (1980-2000) were completed using station data as well as various gridded observational datasets. The RCMs were able to reproduce reasonably well most of the important characteristics of observed spatial patterns and annual cycles of temperature. Average and minimum temperatures were well simulated (biases generally less than 1oC), while maximum temperatures had biases of around 1oC. For precipitation, although the RCMs captured the annual cycle, RegCM4.2 was too dry in Oct.-Nov. (-60% bias), while CCAM was too wet in Dec.- Mar. (130% bias). Both models were too dry in summer and too wet in winter (especially in northern Vietnam). The ability of the ensemble simulations to capture current climate increases confidence in the simulations of future climate.