High-resolution reconstruction of April-September precipitation and major extreme droughts in China over the past ∼530 years.

ABSTRACT

Extreme drought events have increased, causing serious losses and damage to the social economy under current warming conditions. However, short-term meteorological data limit our understanding and projection of these extremes. With the accumulation of proxy data, especially tree-ring data, large-scale precipitation field reconstruction has provided opportunities to explore underlying mechanisms further. Using point-by-point regression, we reconstructed the April-September precipitation field in China for the past ∼530 years on the basis of 590 proxy records, including 470 tree-ring width chronologies and 120 drought/flood indices. Our regression models explained average 50% of the variance in precipitation. In the statistical test on calibration and verification, our models passed the significance level that assured reconstruction quality. The reconstruction data performed well, showing consistency and better quality than previously reported reconstructions. The first three leading modes of variability in the reconstruction revealed the main distribution modes of precipitation over China. Wet/drought and extremely wet/drought years accounted for 12.81%/10.92% (68 years/58 years) and 1.69%/3.20% (9 years/17 years) of the past ∼530 years in China, respectively. Major extreme drought events can be identified explicitly in our reconstruction. The detailed features of the Chongzhen Great Drought (1637-1643), the Wanli Great Drought (1585-1590), and the Ding-Wu Great Famine (1874-1879), indicated the existence of potentially different underlying mechanisms that need further exploration. Although further improvements can be made for remote uninhabited areas and large deserts, our gridded reconstruction of April-September precipitation in China over the past ∼530 years can provide a solid database for studies on the attribution of climate change and the mechanism of extreme drought events.