The average annual precipitation, temperature, and sunshine hours in 38 Japanese cities from 1900 to 2019 obtained from the Automated Meteorological Data Acquisition System (AMeDAS) provided by the Japan Meteorological Agency were used and listed in Table 1 . The latitudes of the 38 AMeDAS observation points are listed. The 38 cities were chosen from AMeDAS locations throughout Japan to ensure a balanced distribution.

Principal component analysis (PCA) attempts to recombine many original indexes with certain correlations into a new set of linearly independent comprehensive indexes to replace the original indexes. PCA is a method of identifying data patterns and expressing data in a manner that highlights their similarities and differences. PCA is a multivariate statistical data mining method used to reduce data size while preserving as many changes in the dataset as possible for faster and more efficient data processing ( Jolliffe, 2011 ). Using PCA, many variables can be reduced to significantly important factors; thus, the resulting factors provide a summary of the original data. We performed PCA to derive climate indices that describe the main spatial features of the climate in Japanese cities using average annual precipitation, temperature, and sunshine hours for 1900-2019. PCA was performed using Python.

The k-means clustering is a method of vector quantization, originally from signal processing, that aims to partition n observations into k clusters, in which each observation belongs to the cluster with the nearest mean (cluster centroid), serving as a prototype of the cluster. The k-means clustering method is widely used for weather classification owing to its simplicity and speed ( Papadimitriou & Tsoukala, 2023 ). In this study, k-means clustering was performed using Python.

We estimated the average temperature anomalies of the 38 Japanese cities for 1960-1989 and 1990-2019 using average temperatures from 1900 to 2019. The calculation results for Tokyo are shown in Figure 1(a) . Changes in the temperature anomalies for 1960-1989 and 1990-2019 are shown in blue and red, respectively. The mean temperature anomalies were 0.394 and 1.377˚C for 1960-1989 (1960M) and 1990-2019 (1990M), respectively. Changes in temperature anomalies for 1940-1979 and 1980-2019 are shown in blue and red, respectively in Figure 1(b) . The mean temperature anomalies were −0.00943 and 1.143˚C for 1940-1979 (1940M) and 1980-2019 (1980M), respectively.

The calculation results for Nagano, Matsumoto, and Hiroshima are shown in Figure 2 , Figure 3 , and Figure 4 , respectively. For every city, 1940M was negative. The 1960M for Nagano and Hiroshima was negative, and for Tokyo and Matsumoto, it was positive. For every city, 1980M and 1990M were positive. There was noticeable warming. The mean temperature anomalies for 1960-1989 (1960M) and 1990-2019 (1990M) are shown in Table 1 .

The explained variances and loadings of the first two principal components calculated for 1960M, 1990M, annual precipitation, and sunshine hours for 1991-2020 in Japanese cities are shown in Table 2(a) . The first and second principal components account for 66.7% of the variance. Figure 5 shows a principal component analysis (PCA) biplot diagram of 1960M, 1990M, average sunshine hours, and annual precipitation. The arrow indicates the relationship between each principal component and feature. The larger the dot product between the axis corresponding to the principal component and the arrow corresponding to the feature, the greater the correlation between the principal component and the feature. In the PCA results, the red arrows are nearly pointing in opposite directions; thus, precipitation and sunshine hours exhibit opposite tendencies. Table 2(b) shows the correlation between 1960M and 1990M (r = 0.51) and an inverse correlation between annual precipitation and sunshine hours (r = −0.03).

The explained variances and loadings of the first two principal components calculated for 1960M, annual temperature, sunshine hours, and precipitation in Japanese cities are shown in Table 3(a) . The first and second principal components account for 74.6% of the variance. Figure 6 shows the PCA biplot diagram of 1960M, annual sunshine hours, temperature, and precipitation. In the PCA results, the red arrows are almost pointing in the opposite direction; therefore, the 1960M and sunshine hours have opposite trends. Table 3(b) shows the strong correlation between temperature and precipitation (r = 0.62) and an inverse correlation between 1960M and sunshine hours (r = −0.25).

The explained variances and loadings of the first two principal components calculated for 1990M, annual temperature, sunshine hours, and precipitation in Japanese cities are listed in Table 4(a) . The first and second principal components account for 70.4% of the variance. Figure 7 shows the PCA biplot diagram of 1990M, annual sunshine hours, temperature, and precipitation. The red arrows of the 1990M and sunshine hours are parallel; therefore, the 1990M and sunshine hours follow the same trend. Table 4(b) shows the correlation between the 1990M and sunshine hours (r = 0.11).

The sunshine hours versus 1960M and 1990M are shown in Figure 8 . When sunshine hours increased, 1960M increased and 1990M decreased. This corresponds to the negative correlation coefficient between 1960M and sunshine hours and a positive correlation coefficient between 1990M and sunshine hours.

The explained variances and loadings of the first two principal components calculated for 1960M, 1990M, 2000M, and 2010M in Japanese cities are shown in Table 5(a) . The 2000M and 2010M are the mean temperature anomalies for 2000-2009 and 2010-2019, respectively. The first and second principal components account for 92.5% of the variance. Figure 9 shows a PCA biplot diagram of 1960M, 1990M, 2000M, and 2010M. The red arrows for 1990M, 2000M, and 2010 indicate almost the same direction; thus, they have the same trends and a strong correlation. Table 5(b) shows the correlation between 1960M and 1990M (r = 0.51).

The k-means clustering of the longitude and latitude of the 38 AMeDAS observation points is shown in Figure 10 . It nearly resembles Japan in shape.

The k-means clustering of the 1960M and 1990M is shown in Figure 11 . It can be classified into four types: high 1960M and high 1990M, which indicates that global warming is progressing rapidly (Sapporo, Tokyo, Kyoto, Osaka, Fukuoka, Nagasaki), low 1960M and low 1990M, global warming is progressing slowly (Nemuro, Ishinomaki, Yamagata, Niigata, Fushiki, Nagano, Karuizawa, Mito, Suwa, Iida, Hamada, Miyazaki, Naha), low 1960M and high 1990M, global warming has accelerated since 1990 (Utsunomiya, Kofu, Okayama, Hiroshima), and normal 1960M and normal 1990M, the rate of warming is normal among the 38 cities (Asahikawa, Aomori, Akita, Kanazawa, Maebashi, Matsumoto, Yokohama, Gifu, Nagoya, Hamamatsu, Kochi, Kagoshima).

The k-means clustering of latitude and 1990M is shown in Figure 12 . The 1990M was distributed at mid-latitudes. This is due to the fact that it contains the high-altitude cities of Iida (516 m), Suwa (760 m), Karuizawa (999 m), and Nagano (418 m). Although Naha and Ishigakijima are located at low latitudes, 1990M is not large.

It is possible to classify the k-means clustering of the 2000M and 2010M; the larger the 2000M, the larger the 2010M. The 2000M and 2010M at each point increase at a similar rate.

The k-means clustering of latitude and temperature is shown in Figure 13 . It is evident that the lower the latitude, the higher the temperature. It can be classified into four types: high latitude and low temperature (Asahikawa, Nemuro, Sapporo, Aomori, Akita), low latitude and high temperature (Miyazaki, Naha, Ishigakijima), normal latitude and normal temperature (Tokyo, Nagoya, Osaka, Hiroshima, Fukuoka, etc.), and normal latitude and distributed temperature (Niigata, Mito, Nagano, Karuizawa, Suwa, Iida, etc.).

The highest points were Nagano (418 m), Karuizawa (999 m), Suwa (760 m), and Iida (516 m). Compared with other locations in the same latitude, the temperature was lower at these highest points.

The k-means clustering of temperature and precipitation is shown in Figure 14 . The higher the annual temperature, the greater the annual precipitation. The locations were classified into four categories: locations with high annual temperature and high precipitation (Fushiki, Kanazawa, Kochi, Miyazaki, Kagoshima, Naha, Ishigakijima), locations with normal annual temperature and normal precipitation (Akita, Niigata, Utsunomiya, Iida, Tyoushi, Tokyo, Yokohama, Gifu, Hamamatsu, Kyoto, Hamada, Osaka, Hiroshima, Matsuyama, Fukuoka, Nagasaki), locations with a little low annual temperature and a little low precipitation (Aomori, Ishinomaki, Yamagata, Nagano, Maebashi, Mito, Matsumoto, Suwa, Kofu, Okayama), and locations with low annual temperature and low precipitation (Asahikawa, Nemuro, Sapporo, Karuizawa).

The k-means clustering of sunshine hours and 1960M, 1990M are shown in Figure 15 and Figure 16 , respectively. The 1990M values are more widely distributed and depend more on sunshine hours. The 1960M is less affected by sunshine hours, whereas the 1990M is more affected. Okayama and Hiroshima are two examples of the long sunshine hours relative to 1960M. However, because Karuizawa and Suwa are higher up, 1960M is less.