The study was conceptualized by M.Z., who also performed data visualization, time series modeling, clustering analysis, attribution analysis, and surveys. T.Z.
assisted with code development, clustering analysis, and formatted the manuscript. H.X., N.W.and L.Y. supervised the manuscript proofreading. Global warming represents one of the most pressing environmental challenges of our time 1,2,3 . The continuous rise in greenhouse gas emissions has led to a steady increase in Earth's average temperature,issues 4,5,6 . Extreme weather events, such as heatwaves and intense precipitation, pose significant threats to human health and ecosystem stability. reports a year-on-year increase in heat-related mortality, with particularly pronounced effects among the elderly and children 7,8 . As global warming intensifies, it is anticipated that the frequency and severity of extreme climate events will increase, thereby posing significant challenges to public health and global environmental systems 9,10,11 . While extensive research has focused on global warming and its impacts on climate and health 12,13,14 , these studies often exhibit significant limitations. Firstly, there is a notable geographical bias, with many studies predominantly concentrating on specific regions such as North America or Europe 15,16,17,18 . For example, V. Reyes-Garc a et al. 19 highlight in their study that there is a paucity of geographic balance in the data from climate change studies, which is also reflected in the disparate directions of climate change studies. This narrow focus potentially overlooks crucial insights from other parts of the world, limiting our understanding of the global nature of climate change. Secondly, there is often a temporal limitation, with many studies focusing on short to medium-term impacts, neglecting long-term trend analysis crucial for comprehensive climate change understanding 20,21 . The work of Miguel Molico 22 suffers from this drawback, as it focuses on the economic impacts of climate change only in the medium term. Furthermore, the lack of a unified global standard for defining and categorizing temperature variations hampers the comparability and broad applicability of research findings 23,24,25,26 . James et al. 27 reviewed methods for identifying regional climate responses to global warming targets and noted the methodological challenges in constraining regional climate signals due to varying data quality and standards across different studies Stein 28 reviewed the challenges at the intersection of climate science and statistics, pointing out that inconsistencies in data collection and standardization limit the ability to make accurate inferences about climate variability and change. This standardization issue is particularly problematic when attempting to synthesize results from various studies or apply findings across different geographical contexts. These limitations collectively impede a comprehensive understanding of global temperature changes and their multifaceted impacts on humanity and the environment. To address these gaps, our study employs a multi-dimensional visual analysis approach using three distinct global temperature datasets. We examine global temperature trends, temperature anomalies, and other relevant metrics, utilizing the Paris Agreement's 1.5 deg C benchmark as a reference point. Our analysis spans from 1880 to the present, with a baseline adjustment to account for pre-industrial warming, thus providing a more comprehensive temporal scope than many existing studies. A key innovation in our approach is the integration of carefully selected time series models, including ARIMA() 29 , SARIMA() 30 , and LSTM 31 , alongside a novel CNN-LSTM(Convolutional Neural Network-Long Short-Term Memory) model for predicting global temperature anomalies over the next century. Importantly, our model selection process prioritizes classic, lightweight structures that are wellsuited to the specific characteristics of our data. This approach reduces model complexity and eliminates the need for advanced feature engineering, addressing a common limitation in existing studies that often rely on overly complex models requiring extensive data manipulation. Our CNN-LSTM model, despite its simplicity, demonstrates exceptional performance, achieving remarkably low mean squared error 32 (MSE: 3x10 6 ), mean absolute error 33 (MAE: 0.002), and a high R-squared value 34 (R 2 : 0.9999). Similarly, for our clustering analysis, we employ the classic DTW-KMeans(Dynamic Time Warping -KMeans) 35  An in-depth attribution analysis of global warming causes based on our predictive models, offering insights into long-term trends often overlooked in shorter-term studies. A time series clustering analysis of temperature anomalies and carbon emissions at the national level using DTW-KMeans, providing nuanced insights for policy-making and addressing the lack of standardization in temperature variation categorization. The collective data presented in these figures demonstrate the disparate effects of global warming across regions. They highlight the unequal distribution of temperature anomalies and the resulting challenges for formulating global climate policy. Therefore, tailored strategies informed by regional specifics are imperative to address these varied impacts effectively. Supplementary Figure 3 elucidates the characteristics and trends of the global temperature dataset for prediction purposes. Supplementary Figure 3a In a nutshell, the dataset exhibits distinct temporal characteristics, including monthly and seasonal variations, and periodic features that reflect both long-term and short-term temperature trends. Its relatively straightforward structure supports the use of classic, lightweight prediction models, minimizing the need for complex feature engineering. Additionally, it captures long-term trend changes and notable local features, encompassing short-term fluctuations and anomalies. Given the temporal and spatial characteristics of the dataset, along with the long-term and short-term periodic variations, prediction models must capture these features. Such models must handle both linear and nonlinear features, as well as cyclical and seasonal changes, to provide accurate predictions. The ability to forecast global temperatures is crucial for understanding climate change and informing policy decisions. This study compares various time series models, including a novel convolutional neural network (CNN) architecture combined with a long short-term memory (LSTM) unit, which demonstrated superior performance in predicting temperature trends. Detailed model performance is presented in The global temperature dataset exhibits complex characteristics, such as pronounced temporal patterns, periodic features reflecting both long-term and short-term trends, and significant local fluctuations. These attributes require a model capable of capturing multi-scale temporal dynamics while maintaining computational efficiency. The CNN-LSTM model addresses these challenges through a meticulously designed architectural framework, depicted in Fig. 4a. The convolutional layer, comprising 32 filters with a kernel size of 3, effectively captures local temporal patterns and short-term fluctuations. This is followed by a ReLU activation function, introducing non-linearity to model complex data relationships.
The subsequent LSTM layer, with 50 hidden units, processes the CNN-extracted features, maintaining sensitivity to long-term dependencies and trends. Finally, a linear output layer maps the LSTM output to temperature predictions. This architecture's strength lies in its synergistic combination of CNN and LSTM components. The CNN efficiently extracts local temporal features without pooling, preserving fine-grained information. The LSTM then processes these features, maintaining temporal order sensitivity crucial for long-term trend analysis. This approach balances model complexity and predictive power, aligning well with the data's structure and eliminating the need for complex feature engineering. Validation on historical data demonstrates the CNN-LSTM model's superior ability to capture both long-term trends and short-term variations compared to traditional LSTM models. This improved accuracy is critical for reliable climate projections and policy guidance. Using this optimized model, we projected global temperature trends for the next century . After adjusting our baseline to align with the Paris Agreement's 1.5 deg C target, our model predicts that global temperatures may reach the critical +1.0 deg C threshold (equivalent to the Paris Agreement's 1.5 deg C target) by 2031. This projection underscores the urgency of implementing effective climate mitigation strategies. In conclusion, the superior performance of our CNN-LSTM model in global temperature prediction stems from its ability to effectively capture multi-scale temporal features without sacrificing computational efficiency. This approach not only provides accurate short-term predictions but also offers reliable long-term forecasts, crucial for informing climate policy and action. The model's projection of imminent critical temperature threshold crossing emphasizes the need for immediate and sustained efforts to mitigate global warming. The primary drivers of global warming can be elucidated through a comprehensive analysis of energy consumption patterns, greenhouse gas emissions, and natural climate variability. Fig. 5 illustrates CO2 emissions from various energy sources from 2000 to 2023, showing that fossil fuel utilization remains the predominant source of CO2 emissions, exhibiting a persistent upward trajectory. This trend correlates directly with the increase in radiative forcing from greenhouse gases shown in Fig. 6, where CO2 makes the most significant contribution. Emissions from fossil fuels, coal, gas, other fossil sources, clean energy, hydro, bioenergy, wind and solar, solar, nuclear, wind, and other renewables. The evolving patterns of energy utilization not only alter atmospheric composition but also influence the global climate system through complex feedback mechanisms. Fig. 6a, depicting temperature anomalies associated with El Nino and La Nino events, exemplifies the short-term impacts of natural climate variability on global temperatures. However, these natural fluctuations are superimposed upon the long-term warming trend driven by anthropogenic greenhouse gas emissions, underscoring the dominant role of human activities in shaping the climate system. The global climate system's response to anthropogenic forcing exhibits complex patterns that vary significantly across space and time.Traditional attribution studies, while crucial for identifying overarching drivers of climate change, often lack the granularity needed to capture regional variations and the temporal evolution of climate indicators. To address this limitation, our clustering analysis seeks to reveal distinct regional patterns in both temperature anomalies and carbon emissions, offering insights into how the primary drivers of global warming identified in attribution studies translate into observable climate trends across diverse geographical contexts. This approach allows us to explore how energy utilization patterns and land-use changes, particularly deforestation, manifest in terms of carbon emissions and temperature anomalies across different parts of the globe. By doing so, we can better understand the differential impacts of these drivers on various regions, accounting for factors such as the level of industrialization, economic development trajectories, and geographical characteristics. This nuanced perspective is essential for developing targeted mitigation strategies and adapting global climate policies to regional contexts. Temperature anomaly clustering yielded three distinct patterns , each corresponding to specific latitudinal bands. Cluster 0, predominantly associated with high-latitude regions, demonstrates a relatively stable temperature profile with moderate warming. Cluster 1, characteristic of mid-latitude countries, exhibits more pronounced temperature increases, particularly post-1970. Cluster 2, primarily representing low-latitude areas, shows the most dramatic temperature rise, especially since the 1980s. This latitudinal gradient in temperature anomalies (Supplementary Figure 5a) aligns with established climate models predicting polar amplification and differential warming rates across latitudes. The confusion matrix reveals a strong negative correlation (-0.67) between latitude and average temperature, whereas the longitudinal correlation is notably weaker (0.14). This geographic asymmetry in temperature patterns is visually reinforced by the temperature contour map , which clearly delineates latitudinal temperature gradients. These findings underscore the critical role of geographical factors in modulating the relationship between carbon emissions and temperature anomalies. This analysis sheds light on the complex, non-linear relationships among carbon emissions, temperature anomalies, and geographical factors. The observed patterns highlight the urgent need for tailored mitigation strategies that account for regional disparities. This study introduces a groundbreaking approach to global warming research, offering novel perspectives and insights through an innovative end-to-end visualization and analysis methodology. By integrating machine learning techniques with established climate science methodologies, we have developed a comprehensive analytical framework that encompasses diverse aspects of temperature data visualization, attribution analysis, regional clustering, and future projections. This integrated approach not only mitigates the geographical bias and time constraints inherent in existing studies but also provides a holistic framework for understanding the complex dynamics of global warming. Our visualizations reveal extensive spatiotemporal patterns of climate change, offering an intuitive grasp of global warming and facilitating access to complex climate data. The hybrid CNN-LSTM model for temperature forecasting challenges the assumption that only complex models are essential for climate prediction, demonstrating that simple, well-designed models can effectively capture both short-and longterm climate trends. Projections for the next century indicate an acceleration of global warming, with average temperatures potentially surpassing pre-industrial levels by 1.5 deg C as early as 2031 under highemission scenarios, underscoring the urgency of tailored climate strategies. Attribution analysis identifies energy use and deforestation as key human drivers of warming, while DTW-KMeans clustering reveals regional climate patterns, emphasizing the importance of latitude in climate response and informing targeted mitigation strategies.
The innovation of this study lies not only in its methodology but also in its significant contributions to climate science and policy-making. By integrating global attribution and regional clustering analyses, we bridge the gap between macro-level driving factors and micro-level climate change manifestations. All in all, this paper is the first to propose an end-to-end visualization analysis, establishing a closedloop analytical approach. This comprehensive method lays the foundation for future research and more targeted climate policies, promising to develop more effective strategies for mitigating and adapting to climate change at both global and regional scales. Looking ahead, we plan to incorporate additional climate variables and develop higher-resolution regional climate models to deepen our understanding of the complex dynamics of global warming. The application of this model demonstrates its potential to provide critical insights into climate change, aiding policymakers and scientists in formulating more nuanced and effective strategies. This paper employs a multitude of comprehensive databases to facilitate the end-to-end visualization and analysis of global temperature changes. The initial dataset employed is the "Climate Change Indicators" dataset from Kaggle (1961-2022). This offers detailed climate change indicators and country codes, with annual updates ensuring the information's timeliness. The dataset can be accessed via the following link: Subsequently, the "" from the is employed, incorporating 1.6 billion temperature records and integrating additional short-term meteorological observations through specialized methods (Dataset link: The aforementioned datasets facilitate a comprehensive analysis of temperature data characteristics. To analyze the causes of global warming, a Kaggle dataset is employed, which comprehensively examines temperature, carbon dioxide emissions, and other key climate indicators to identify the primary drivers of global warming (Dataset link: In cluster analysis, the global temperature anomaly dataset based on ECMWF ERA5 project data (using 1991-2020 as the baseline) is employed, along with the carbon dioxide and greenhouse gas emissions dataset for each country from Our World in Data. This diverse collection of datasets provides a robust foundation for our multi-dimensional analysis (Dataset links: and The Paris Agreement, ratified in 2015, aims to limit global warming to within 1.5 degrees Celsius above pre industrial levels 36,37,38,39 . This science-based threshold indicates that maintaining global temperatures below this level will significantly mitigate the risks and impacts of climate change. has conducted a crucial analysis of the dangers associated with the 1.5 deg C and 2 deg C thresholds mentioned in the Paris Agreement, as illustrated in does not specify a particular pre-industrial period for calculating temperature anomalies, though the years 1850 to 1900 are widely and uses the period 1951-1980 as the reference for temperature anomalies. Since this reference period is not pre-industrial, it is necessary to adjust the temperature anomaly baseline to accurately reflect the difference from pre-industrial levels. The period 1951-1980 is already warmer than the pre-industrial baseline, so adding 1.5 deg C directly does not correspond to the Paris Agreement's target. Visual inspections and estimates presented in this paper indicate that the global mean temperature in the mid-20th century was approximately 0.3 deg Cto 0.4 deg C higher than the pre-industrial level. Considering this discrepancy, the baseline is adjusted downwards by 0.5 deg C , with +1.0 deg C used as the revised baseline in this analysis. In this paper, a thorough statistical visualization analysis of the datasets revealed a relatively simple data structure, prompting the selection of classic and commonly used models for both prediction and clustering analyses. This approach minimized model complexity and obviated the need for sophisticated feature engineering. For time series prediction, we evaluated the ARIMA, SARIMA, and LSTM models. ARIMA is a statistical model used for time series forecasting. It combines Autoregressive (AR), Integrated (I), and Moving Average (MA) components to capture the temporal dependencies in the data. The basic equation is as follows: where is the current value of the time series, and are the autoregressive and moving average coefficients, respectively, and is the white noise. SARIMA extends the ARIMA model by incorporating seasonal components to capture the seasonal fluctuations in the data. The equation is: where () and ( ) are polynomials representing non-seasonal and seasonal components, respectively, and is the backshift operator. where: is the candidate cell state, is the input gate, is the forget gate, is the output gate, is the cell state, h is the hidden state, , , , are weight matrices for the cell state, input gate, forget gate, and output gate, respectively, , , , are input weight matrices. However, the LSTM model introduces noise that is unrelated to temperature prediction and is also affected by larger and smaller values of short-term fluctuations in the time-series data. This results in poor predictions. Inspired by the success of CNN-LSTM models in autonomous driving 41,42,43 , where they excel in capturing the spatiotemporal dynamics of traffic participants, we adapted this approach for global temperature change prediction 44 . CNNs are adept at processing image data and can extract local patterns and features from time series through local convolution operations, which is crucial for capturing shortterm temperature trends 45,46,47 . By leveraging CNNs 48 to extract local time series features and LSTM to model global temporal dependencies, we aimed to enhance prediction accuracy and robustness 49,50 .To align with our data characteristics, we designed a simple CNN-LSTM structure that eliminated the need for complex feature engineering. The proposed CNN-LSTM model's computational equations and pseudocode are provided below to illustrate the implementation. For clustering analysis, we employed the DTW-KMeans algorithm, a hybrid of Dynamic Time Warping (DTW) 51 Our primary code and weight are available at The authors declare no competing interests. For detailed Supplementary Information, please refer to the associated PDF document. Our primary code and weight are available at The authors declare no competing interests. For detailed Supplementary Information, please refer to the associated PDF document.