Characteristics of compound high temperature and drought events and assessment of standardized economic losses across China from 1984 to 2021

doi:10.7523/j.ucas.2025.005

Abstract

Abstract:

As socioeconomic development progresses， the interaction between climatic hazards and socioeconomic conditions amplifies substantial economic losses at varying scales. This study focuses on the characteristics and economic impacts of compound drought-heatwave events in China from 1984 to 2021. Using a standardized methodology， we developed a drought intensity-loss ratio model to isolate the influence of socioeconomic development on drought-related economic losses， providing a scientific basis for loss estimation and regional disaster prevention strategies. The results indicate that 1984 to 1990 was a period of high drought frequency， with the primary drought centers located in Southwest China， the central Huang-Huai region， and the upper Yangtze River， where economic losses were particularly significant. While raw economic losses increased over time， losses adjusted for population and economic growth showed a declining trend， closely aligning with the spatiotemporal distribution of droughts. Population growth and economic development are identified as the main drivers of the significant increase in China’s drought-related economic losses. The standardization approach effectively reveals the actual impacts of drought events. Based on these findings， this study constructed a drought intensity-loss ratio model to better characterize the relationship between natural drought variability and economic losses， offering a robust framework for accurately assessing drought impacts and supporting disaster mitigation strategies.

Key words: compound high-temperature and drought events, drought intensity, direct economic loss, normalization method, loss ratio

CLC Number:

P429

Yu ZHOU, Lijuan HUA, Linhao ZHONG, Yang YANG, Zhaohui GONG, Yuxin ZHOU. Characteristics of compound high temperature and drought events and assessment of standardized economic losses across China from 1984 to 2021[J]. Journal of University of Chinese Academy of Sciences, 2026, 43(4): 519-530.

Figures/Tables 9

Table 1 Classification of indices

程度	SPI	STI	程度	SCEI/SDHI
无	（-0.5， ∞）	（-∞， 0.5）	异常	［-0.5，-0.8）
轻	［-0.5， -1.0）	［0.5， 1.0）	中度	［-0.8，-1.2）
中	［-1.0，-1.5）	［1.0， 1.5）	严重	［-1.2，-1.6）
重	［-1.5，-2.0）	［1.5， 2.0）	极端	［-1.6，-2.0）
特	（-∞， -2.0］	$[2.0,$ ∞）	极致	（-∞， -2.0］

Table 1 Classification of indices

程度	SPI	STI	程度	SCEI/SDHI
无	（-0.5， ∞）	（-∞， 0.5）	异常	［-0.5，-0.8）
轻	［-0.5， -1.0）	［0.5， 1.0）	中度	［-0.8，-1.2）
中	［-1.0，-1.5）	［1.0， 1.5）	严重	［-1.2，-1.6）
重	［-1.5，-2.0）	［1.5， 2.0）	极端	［-1.6，-2.0）
特	（-∞， -2.0］	$[2.0,$ ∞）	极致	（-∞， -2.0］

Fig.1 Temporal changes and spatial distribution of the annual average SPI-12 in China from 1984 to 2021

Fig.2 Regional average time series of SCEI， SDHI， SPI， and STI during summer in China from 1984 to 2021 and the bubble plots of the SCEI and SDHI

Table 2 List of correlation coefficients between indices

	STI	SPI	SCEI	SDHI
STI	1	-	-	-
SPI	-0.165	1	-	-
SCEI	-0.787^*	0.634^*	1	-
SDHI	-0.793^*	0.656^*	0.920^*	1

Fig.3 Interannual variations of direct economic losses due to drought and their proportion to GDP， sliding T-test， and red noise spectrum analysis from 1984 to 2021

Table 3 Mean annual drought losses and their share in GDP

	DEL/亿元	DEL/GDP/%	DEL/GDP增量/%
原始	322.5	0.20	1.04
DEL_CPI	450.9	0.46	1.46
DEL_CNM	4 862.9	21.56	15.76
DEL_ANM	2 051.1	5.46	6.65

Fig.4 Spatial distribution of DEL_Original and DEL_normalized caused by drought in China from 1998 to 2020

Fig.5 Spatial distribution of trend changes in annual average DEL_Original and DEL_normalized caused by drought from 1998 to 2020

Fig.6 Spatial distribution of correlation coefficients between average annual drought intensity and direct economic loss in different provinces from 1998 to 2020 and the relationship between them and economic loss rate

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