**1. Introduction**

The climate–war nexus in historical China has been widely addressed by academic communities over the past decade [1–19]. Scientists have mostly focused on this nexus from a temporal or time-series angle, whereas the spatiality of war and its connection with climate change has rarely been investigated. Recently, our group determined that in imperial China, (1) geopolitical variables, such as the boundary between agriculturalists and pastoralists, size of agricultural empire, battle location, and the direction of war, were affected by multi-centennial precipitation fluctuation [15]; (2) the distributions of natural disasters (flood and drought) and their social impacts (famine, cannibalism, and war) were influenced by population on provincial and decadal scales [20]; and (3) secular temperature variation fundamentally regulated the spatial disparities of war via controlling agricultural and pastoral productivity [21]. However, research on the spatiotemporal pattern and its dynamic process of war has not been fully conducted.

In this study, we aimed to solve this problem by examining the linkage between climate change and the focus (or hot spot) of war in imperial China. Using the comprehensive official history and well-preserved local and private records in China since ancient times, a few native scholars have discovered the focus (similarly, geographical pivot, or strategic area) of war. For example, Song [22] divided wars in the imperial era into frontier and interior wars, and the pivots of the latter were distributed in the western Henan Corridor, the south of the Huai River (also known as the Jianghuai region), and Jing–Xiang (present-day northern Hubei). Rao [23] introduced an irregular chessboard pattern of war from a military geographical view, in which there were nine strategic areas—four corners: Guanzhong, Hebei, Southeast China, and Sichuan; four foci/pivots: Shanxi, Shandong, Hubei, and Hanzhong; as well as the heartland, the Central Plain. Wang [24] extracted information on the spatial distribution and the shift of the focus area of war from poems in 618–765 CE (i.e., the early Tang dynasty), which implied the potential value of classic literature. Leng [25] looked into the frontier conflicts between the central governments and northern minorities during the imperial age and found that the focus areas had moved from the Hetao region and the Hexi Corridor of Northwest China since the Qin and Han dynasties to the Sixteen Prefectures of Yanyun and the western Liaoning Corridor of Northeast China after the Tang dynasty. These findings, however, are all qualitative and do not contain any climatic variables.

To fill in the research gaps, the technique in ArcMap, Emerging Hot Spot Analysis (EHSA), was applied in this study. Developed by the Environmental Systems Research Institute, Inc. (ESRI), EHSA identifies the spatial trends and distributions of di fferent types of hot spots from data points. It has been employed by some experts to unveil spatial patterns with time, such as detecting public sentiment from geotagged photo collections in San Francisco in 2006–2015 and showing that di fferent emotions (anger, disgust, fear, joy, sadness, and surprise) have distinct spatial distributions [26], as well as the spatiotemporal associations between a community greening program and neighborhood crime rates in Flint, Michigan, in 2005–2014 [27]. Other examples include the spatiotemporal analysis of changes in lode mining claims around the McDermitt Caldera, northern Nevada, and southern Oregon, in 1976–2010 [28], the expected trend in the occurrence of pulmonary tuberculosis cases from Hamadan Province, Iran, during 2005–2013 [29], spatial patterns of crimes (larceny and aggravated assault) in Miami-Dade County, Florida, from 2007 to 2015 [30], and statistically significant temporal-spatial trends of forest loss in Brazil, Indonesia, and the Democratic Republic of Congo between 2000 and 2014 [31]. Thus, by integrating time and space domains, EHSA was suitable for the task of uncovering the hot spots of war in China from 1 to 1911 CE. Compared with the aforementioned studies that only covered several decades at most, this work is the first to use EHSA on a long time scale. Furthermore, we made a methodological breakthrough by using the analysis with a climatological background, which may lay a foundation for further exploration by researchers in related fields.

The structure of this paper is as follows. Data sources and data processing, which include the cyclic division schemes for temperature and precipitation series, and the statistical tools, such as the Global Moran's *I* and EHSA, are introduced in Section 2. Distributions of the hot spots of three kinds of war—all war, the conflict between agriculturalists and pastoralists ("agri-nomadic conflict"), and rebellion under di fferent climatic phases (warm versus cold/wet versus dry) in ancient China are presented in Section 3. Some discussions about the e ffects of climatic and other non-climatic factors on war hot spots, and our conclusions, are provided in Sections 4 and 5, respectively.

#### **2. Materials and Methods**

#### *2.1. Data Source and Data Processing*

The data used in this study included climatic series and battle coordinates. The procedures of cycle divisions for temperature and precipitation sequences are stated in detail.
