Assessing and modifying the formula for estimating the conditional transition probabilities and daily precipitation variance under the influence of climate change

Authors: XIAO, YUKUB - Jiangxi Agricultural University; HE, XIAOWU - Jiangxi Agricultural University; WEI, HAIYAN - University Of Arizona; Zhang, Xunchang; LI, FENGYING - Jiangxi Agricultural University; WANG, YANYAN - Jiangxi Agricultural University; ZHANG, YIZHI - Meteorological Science Institute Of Jiangxi Province, China

Submitted to: Journal of Applied Meteorology and Climatology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/17/2025
Publication Date: 6/6/2025
Citation: Xiao, Y., He, X., Wei, H., Zhang, X.J., Li, F., Wang, Y., Zhang, Y. 2025. Assessing and modifying the formula for estimating the conditional transition probabilities and daily precipitation variance under the influence of climate change. Journal of Applied Meteorology and Climatology. 64(6):626-636. https://doi.org/10.1175/JAMC-D-24-0193.1.
DOI: https://doi.org/10.1175/JAMC-D-24-0193.1

Interpretive Summary: A Climate Generator (a computer program named CLIGEN) is used in conjunction with projections of Global Climate Models to predict the impacts of climate change on hydrology and soil erosion. CLIGEN requires the input parameters of the conditional transition probabilities of precipitation (odds of precipitation occurrence) and daily precipitation variance. The objective of this work is to improve the accuracy of the estimation of these parameters, and thereby to enhance the prediction of future change of precipitation. This work introduced a weighting method and a regression method to modify the transition probabilities of precipitation and precipitation variance, respectively. All methods were validated using 100-year daily precipitation data from Tucson in a semi-arid region of the United States, and 60-year precipitation data from five stations in a humid region in China. Results showed that the weighting method improved estimation of conditional transition probabilities in semi-arid region, and the regression method improved precipitation variance estimation for most stations. The improved approaches would be useful to climatologists and hydrologists for estimation of climate change impact on natural resources. USDA is an equal opportunity provider and employer.

Technical Abstract: CLIGEN (Climate Generator) is commonly used in conjunction with GCMs (Global Climate Models) to predict the impacts of climate change on hydrology and soil erosion. In the predicting process, accurate assessments require the use of downscaling methods to estimate the parameters of the conditional transition probabilities of precipitation and daily precipitation variance. The objective of this paper is to improve the accuracy of the estimation of the above parameters, and thereby to enhance the prediction of future change of precipitation. Building upon previous works of Two-point and Four-point Methods on conditional transition probabilities and Approximation Method on daily precipitation variance, this study introduces Weighting Method and Regression Method to develop a modified approach. All methods were validated using 100-year daily precipitation data from Tucson in a semi-arid region of the United States, and 60-year precipitation data from five stations in a humid region in China. Results showed that all three methods demonstrated strong performance in estimating conditional transition probabilities. However, Two-point and Four-point Methods underestimated the probability of wet days following wet days in semi-arid region. The Weighting Method reduced the biases in semi-arid region. The Approximation Method exhibited errors in estimating the daily precipitation variance indicated by negative model efficiency (ME) of self-test in both semi-arid and humid regions, while the Regression Method showed a strong relationship between daily and monthly precipitation variance with a high ME of self-test. High ME values of validation tests in both semi-arid and humid regions demonstrated that the Regression Method is highly applicable in both climatic regions. USDA is an equal opportunity provider and employer.