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ARS Home » Plains Area » El Reno, Oklahoma » Grazinglands Research Laboratory » Agroclimate and Natural Resources Research » Research » Publications at this Location » Publication #358567

Research Project: Uncertainty of Future Water Availability Due to Climate Change and Impacts on the Long Term Sustainability and Resilience of Agricultural Lands in the Southern Great Plains

Location: Agroclimate and Natural Resources Research

Title: Challenges and potential solutions in statistical downscaling of precipitation

Author
item CHEN, JIE - Wuhan University
item Zhang, Xunchang

Submitted to: Climatic Change
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/30/2021
Publication Date: 4/24/2021
Citation: Chen, J., Zhang, X.J. 2021. Challenges and potential solutions in statistical downscaling of precipitation. Climatic Change. 165(63):1-19. https://doi.org/10.1007/s10584-021-03083-3.
DOI: https://doi.org/10.1007/s10584-021-03083-3

Interpretive Summary: Climate downscaling is an effective technique to bridge the gap between low spatial resolutions of climate model outputs and high resolutions of climate data requirement by crop models for assessing local climate change impacts on crop production. However, downscaling of temporal sequence and extremes of daily precipitation, and handling of non-stationary precipitation in future conditions are considered as the common challenges for most statistical downscaling methods. Here we reviewed the three key challenges in statistical downscaling and proposed potential solutions. We used ten stations around the world as examples for a proof of concept. Using a stochastic Markov chain to generate daily precipitation occurrence is an effective approach to simulate the temporal sequence of daily rainfall. Downscaling precipitation extremes can be achieved by adjusting the skewness coefficient of a probability distribution, as they are highly correlated. Non-stationarity in precipitation downscaling can be handled by adjusting parameters of a probability distribution according to future precipitation change signals projected by climate models. The perspectives proposed in this paper are of great significances to agricultural modelers in using climate model outputs for assessing local and site-specific climate change impacts, especially on future food security.

Technical Abstract: Downscaling is an effective technique to bridge the gap between climate model outputs and data requirements of impact models for assessing local and site-specific climate change impacts, especially on future food security. However, downscaling of temporal sequence and extremes of daily precipitation, and handling of non-stationary precipitation in future conditions are considered as the common challenges for most statistical downscaling methods. Here we reviewed the three key challenges in statistical downscaling and proposed potential solutions. We used ten stations around the world as examples for a proof of concept. Using a stochastic Markov chain to generate daily precipitation occurrence is an effective approach to simulate the temporal sequence. Downscaling precipitation extremes can be achieved by adjusting the skewness coefficient of a probability distribution, as they are highly correlated. Non-stationarity in precipitation downscaling can be handled by adjusting parameters of a probability distribution according to future precipitation change signals projected by climate models. The perspectives proposed in this paper are of great significances in using climate model outputs for assessing local and site-specific climate change impacts, especially on future food security.