Cross Wavelet Research Articles

Interannual Covariation of the North American and African Summer Monsoons

ABSTRACTThe North American summer monsoon (NASM) and the North African summer monsoon (NAFSM) are two vital subsystems of the global monsoon. To date, the potential inter‐monsoon relationship between the NASM and NAFSM has not been fully understood. To fill this gap, we investigate the NASM–NAFSM relationship on the interannual timescale during the period of 1979–2022. Based on statistical methods (including correlation, empirical orthogonal function and cross wavelet analyses), we identify a noteworthy interannual covariation of the NASM and NAFSM. This observed NASM–NAFSM covariation can be explained by atmospheric circulation anomalies associated with sea surface temperature (SST) anomalies in the tropical central‐eastern Pacific and tropical Atlantic, suggesting the critical roles of tropical Pacific–Atlantic SST anomalies in shaping the NASM–NAFSM covariation. The results of Coupled Model Intercomparison Project Phase 6 (CMIP6) models indicate that a model's ability to simulate the NASM–NAFSM covariation tends to be related to its ability to reproduce the modulating effects of the tropical Pacific–Atlantic SST anomalies. These results have potential implications for seasonal forecasts of the NASM and NAFSM variations, suggesting that the NASM and NAFSM can be considered simultaneously in climate predictions.

Real-time lithology identification from drilling data with self & cross attention model and wavelet transform

Accurate lithology identification during drilling operations is pivotal for optimizing drilling efficiency and informed decision-making. Traditional methods, such as mud logging, Logging While Drilling (LWD), and Measurement While Drilling (MWD), often face challenges such as delays and inaccuracies, lacking consistent utilization of real-time engineering data across all drilling sites. Consequently, these methods frequently overlook the complex interactions of influencing factors, resulting in underutilized geological information that could significantly enhance operational outcomes. This paper introduces the SelfAttention-CrossWT (SACWT) model, an innovative approach that integrates self-attention and cross-attention mechanisms with Wavelet Transform (WT) to utilize real-time drilling parameters, including Weight on Bit (WOB) and Rate of Penetration (ROP). The SACWT model effectively harnesses both temporal and frequency-domain characteristics of drilling data, facilitating comprehensive extraction of latent patterns and dynamics, which substantially improves lithology identification accuracy. The output of the model is the real-time lithology classification at the drill bit's depth. The performance of the SACWT model is compared with Long Short-Term Memory (LSTM) networks and a Self-Attention (SA) model that does not utilize frequency-domain data. When applied to a dataset from the Volve oil field, the SACWT model achieved identification accuracies of 87% and 91% in two separate test wells, outperforming traditional methods. These results underscore the model's efficacy and the significant potential of utilizing real-time drilling data for enhanced lithological characterization. This breakthrough offers a robust framework for future exploration and development operations, providing a more precise, efficient, and cost-effective approach to geological analysis.

Wet and dry spell induced changes in the soil CO2 effluxes of Pine and Oak ecosystems of Central Himalaya: a comparative assessment for monsoon and winter seasons.

Soil efflux of CO2( ) is known to be dependent on natural drying and rewetting of the soil. Although the central Indian Himalayan region is predominantly occupied with two ecosystems, i. e. Pine (Pinus roxburghii) and Oak (Quercus leucotrichophora), differences in their dynamics and responses of to varying wet and dry spells were hardly known. To address this knowledge gap, this study provides a comparative assessment of variability from Pine and Oakecosystems of central Himalaya as a response to rainfall induced wet and dry spells of monsoon and winter seasons. The data presented in this study are collected for242 days of 2021-22 that include monsoon and winter seasons from a Pine and an Oak sites. The mean sof Pine and Oak sites are found to be 3.95(± 0.02) and 3.61(± 0.01) μmol.m-2.s-1, respectively. We find that the winter reduction in the in comparison to monsoon at the Pine site (78%) is more substantial than at Oak site (64.6%). The cross wavelet spectra of and monsoon rainfall amount at the Oak site, unlike the Pine site, indicate a negative relationship. The rainfall spell duration and amount of monsoon wet spells are noted to have an inverse relationship with at both sites, although, increasing rainfall spell duration in winter is noted to increase at Pine and Oak sites. Similarly, increasing is observed with increasingdry spells of monsoon at both sites. Results of this study indicate that in comparison to Oak, variability at Pine ecosystem is primarily driven by abiotic factors wherein wet spell is a major determinant.

Assessing teleconnection influences on the spatial and temporal patterns of meteorological drought in Northwest China

ABSTRACT This study delves into the complex relationship between teleconnection patterns and meteorological drought in Northwest China, highlighting the crucial influence of global circulation indices on regional drought dynamics. By analyzing precipitation and temperature data from 1962 to 2022 using the CN05.1 datasets and incorporating various global circulation indices, the study employs the Standardized Precipitation Evapotranspiration Index (SPEI) to delineate drought conditions. Utilizing the Modified Mann-Kendall test and segmented models for trend and change point detection, along with cross wavelet transforms (XWT) and wavelet coherence (WTC) analysis to examine the impact of 15 global circulation indices, the study uncovers significant spatial and temporal climatic variations. Findings indicate a significant increase in temperature and precipitation, with March-April-May (MAM) season showing pronounced drought severity mainly due to a significant temperature rise with a value of 0.0420 °C/year. Change point analysis reveals pivotal shifts in climate, highlighting the region’s susceptibility to climate change. The study identified strong correlations between drought occurrences and global circulation indices like the Artic Oscillation (AO), Atlantic Multidecadal Oscillation (AMO), Pacific Decadal Oscillation (PDO) and the Southern Oscillation Index (SOI), especially within a 4–8 year timeframe, pointing to the significant role of teleconnections in affecting local drought conditions. These insights are vital for formulating effective drought management and climate adaptation strategies in arid and semi-arid regions, offering valuable guidance for policymakers and researchers focused on improving water resource management and enhancing climate resilience in such vulnerable environments.

GRACE based groundwater drought evaluation of Ganga Basin and analysis of drought propagation using wavelet based quantitative approach

The Ganga River Basin which is the home of almost half a billion people have plunged into groundwater drought due to anthropogenic activities. Hence groundwater drought assessment and its propagation from the meteorological drought is highly required for the Ganga River Basin. This paper focuses on the evaluation of historical groundwater drought using Gravity Recovery and Climate Experiment (GRACE) and Global Land Data Assimilation System (GLDAS) dataset, further to obtain the drought propagation times. Traditionally the drought propagation time is obtained from the correlation between groundwater drought time series and various scales of meteorological time series. However, the GRACE-derived groundwater drought index (GGDI) showed lesser correlation with the Standardized Precipitation Index (SPI) / Standardized Precipitation Evapotranspiration Index (SPEI) due to the presence of consistent trend in the GGDI series. Hence, a novel quantitative approach using Cross Wavelet Transform (XWT) is introduced to determine the drought propagation time which can devoid of the contribution of anthropogenic activities. Extracting the significant power area of XWT of GGDI with SPI/SPEI of different scales led to the determination of groundwater drought propagation time. The results showed Groundwater Storage Anomaly (GWSA) has a steep downtrend for Upper Gangetic Basin (UGB) (−26.2 mm/year) and Yamuna Chambal Basin (YCB) (−21.8 mm/year). It was observed that UGB and YCB faced groundwater drought from 2017 to 2022. The wavelet analysis showed that the drought propagation time of YCB is 14 months, UGB is 17 months, and the Lower Gangetic Basin (LGB) is 21 months. The frequency domain analysis of the drought signals suggested YCB had a faster response to the meteorological forcing, and LGB had the slowest response.

Trivariate copula functions for constructing a comprehensive atmosphere-land surface-hydrology drought index: A case study in the Yellow River basin

The current univariate and multivariate drought indices cannot fully reflect the multiple processes of meteorological-hydrological-agricultural drought. So it is important to construct a comprehensive drought index for drought monitoring in a changing environment from the perspective of the whole process of the natural water cycle “atmosphere-land surface-hydrology”. In this study, based on the standardized indices (SCI) framework and trivariate Copula functions, a multi-scale comprehensive drought index (SPERSI) integrating rainfall, temperature, runoff and soil moisture is proposed, which can synthetically reflect meteorological, hydrological and agricultural droughts. The spatial and temporal variations of drought in the Yellow River Basin (YRB) from 1961 to 2019 were investigated, and then the links between SPERSI and some remotely correlated factors were revealed by using the multiple cross wavelet transform technique. Results show that: (1) the SPERSI constructed by fusing rainfall-temperature-runoff-soil moisture information can sensitively and effectively capture the onset, persistence and termination of drought; (2) the SPERSI exhibits good performance in detecting integrated drought events in the YRB with probability of detection (POD) > 0.75, false alarm rate (FAR) < 0.04 and critical success index (CSI) > 0.73; (3) From 1961 to 2019, drought in different time scales of above Longyangxia (AL) and Longyangxia to Lanzhou (LL) zones in the YRB both show a slight mitigation trend, while the drought in remaining subzones show a trend of intensification; (4) Multiple cross-wavelet results indicate that AO is the dominant factor of drought in the Yellow River Basin, followed by ENSO. In general, SPERSI results are reliable and can provide basis for drought decision-making.

Analyzing Covid-19 Pandemic Through Cases and Spillover Effect: Cross Wavelet Transformation of Mobility in the Countries

AbstractThis research examines the relationship between daily Covid-19 cases and public mobility for six different places, where mobility can be high, in Brazil, the UK, and the USA. We tried to show how the spreading effect of infectious diseases occurs in places where human mobility is intense. The data consists of daily data covering the periods from 28/02/2020 to 08/02/2021, which includes the database of Our World in Data and Google Mobility Reports. Basically, there is a positive correlation between mobility and case numbers. Results in the paper differ from country to country. That is to say, mobility in grocery stores and pharmacies can be said to have an impact on the number of cases in all three countries. Brazil is the country where mobility in parks has the strongest impact. The country which has the lowest frequency in retail outlets is America.

Time-frequency co-movements between commodities and global economic policy uncertainty across different crises

Commodity futures constitute an attractive asset class for portfolio managers. Propelled by their low correlation with other assets, commodities begin gaining popularity among investors, as they allow to capture diversification benefits. This comprehensive study examines the time and frequency spillovers between the Economic Policy Uncertainty [1] and a broad set of commodities encompassing ferrous, non-ferrous, and precious metals, food, and energy commodities over a period from December 1997 to April 2022, which includes various political, economic and health crises.The novelty of this research lies in its extensive temporal and categorical coverage, providing an understanding of how different types of commodities respond to various crises. Furthermore, our study breaks new ground by employing wavelet analysis to gain detailed insights in both time and frequency domains in the financial time series of interest, providing a deeper understanding of the co-movements and lead-lag relationships. Specifically, we introduce the Cross Wavelet Transform (XWT) and Wavelet Coherence (WTC) analysis.Our findings demonstrate that not all crises uniformly impact commodities. Notably, during the global financial crisis and the COVID-19 pandemic, co-movements between commodities became significantly stronger. These results highlight the heterogeneity within the commodity asset class, where individual commodities exhibit diverse underlying dynamics. Importantly, the proposed methodology facilitates the extraction of robust results even when dealing with nonlinearities and nonstationary time series data. Consequently, our work offers valuable insights for policymakers (including regulatory bodies), investors, and fund managers.

Ground Deformation Monitoring Using InSAR and Meteorological Time Series and Least-Squares Wavelet Software: A Case Study in Catania, Italy

Abstract. Persistent Scatterers Interferometric Synthetic Aperture Radar (PS-InSAR) is an advanced satellite remote sensing technique which allows an effective monitoring of ground movement. In this work, PS-InSAR time series as well as precipitation and temperature time series in a region in Catania, Italy are utilized during 2018–2022, and their possible interconnections with land subsidence/uplift due to groundwater level change are investigated. First, the potential jumps in the displacement time series are removed, and then the Sequential Turning Point Detection (STPD) is applied to estimate the times when the velocity of the displacement time series changes. The results show a significant correlation between the frequency of turning points in displacement time series and precipitation trend change, particularly during the winter season. Furthermore, the Least-Squares Cross Wavelet Analysis (LSCWA) is applied to estimate the coherency and phase delay between the displacement and weather cycles in the time-frequency domain. The annual cycles of displacement and temperature show more coherency than the ones of displacement and precipitation across the study region. The results presented herein are important for infrastructure and water management planning.

Interpersonal Motor Coordination in Children with Autism and the Establishment of Machine Learning Models to Objectively Classify Children with Autism and Typical Development

BackgroundThe global prevalence of autism spectrum disorder (ASD) is around 1%. Yet the current diagnosis of ASD mainly depends on clinicians' experience and caregivers' report, which are subjective, time consuming, and labor demanding. An objective and efficient way to diagnose ASD is urgently needed. The objective of this study was to quantify an omnipresent yet least studied behavioral characteristic in children with ASD – interpersonal motor coordination (IMC), and to investigate the feasibility of using IMC related features to identify ASD by implementing machine learning algorithms. MethodsTwenty children with ASD and twenty-three children with typical development (TD) were filmed in a conversation with an interviewer. Motion energy analysis was implemented to obtain the movement time series, and cross wavelet analysis (CWA) quantified the level of IMC at different movement frequencies. Machine learning algorithms were utilized to examine whether these two groups of children could be accurately classified using features of IMC. ResultsStatistical analysis revealed reduced IMC in the ASD group at relatively high movement frequencies. The establishment of machine learning (ML) models showed that the maximum classification accuracy was 85.37% (specificity = 95.24%, sensitivity = 75.00%) using five original coherence values computed with CWA. In addition, the classification accuracy could be improved to 92.68% (specificity = 95.24%, sensitivity = 90.00%) with three novel features created by taking the sum of statistically significant features. ConclusionsChildren with ASD demonstrated an atypical profile of IMC, and IMC could be used to objectively classify children with ASD and TD. In addition, our analyses showed that creating novel features based on statistically significant features could help improve classification performance. It is proposed that such economic, contactless, and calibration-free approach to data collection might well serve both ASD research and practice, particularly early objective identification. However, this study could be improved with respect to larger sample size with balanced gender ratio and different severity.

The possible impact of solar activity on rainfall in Nepal: A case study

This paper investigates the possible relationship between solar activity and monthly rainfall in Pokhara and Kathmandu cities in Nepal from the year 1968 to 2023. The study uses Cross Correlation Analysis (CCA) and wavelet analysis, the Cross-Wavelet Transform (XWT) and Wavelet Transform Coherence (WTC), to analyze the time series data. The CCA shows a strong positive correlation between the solar cycle and rainfall trend, with correlation coefficients of 0.77 and 0.76 and time lags of 18 months and 14 months at Pokhara and Kathmandu, respectively. The p-values, calculated to be < 0.0001 through the t-test, indicate that the observed correlations are statistically significant. The XWT results show a long-term ∼ 11-year common periodicity in solar cycle and rainfall. The WTC analysis confirms the local correlation between these two time series in time–frequency domain at a common periodicity. The results indicate that the monthly rainfall pattern in Nepal is closely related to the solar cycle pattern. Thus, solar activity likely plays a role in influencing rainfall trends in Nepal. This study shows that solar-climate interaction on a regional scale is complex and emphasizes the importance of understanding these relationships for predicting and managing climate change.

Analyzing vegetation health dynamics across seasons and regions through NDVI and climatic variables

This study assesses the relationships between vegetation dynamics and climatic variations in Pakistan from 2000 to 2023. Employing high-resolution Landsat data for Normalized Difference Vegetation Index (NDVI) assessments, integrated with climate variables from CHIRPS and ERA5 datasets, our approach leverages Google Earth Engine (GEE) for efficient processing. It combines statistical methodologies, including linear regression, Mann–Kendall trend tests, Sen's slope estimator, partial correlation, and cross wavelet transform analyses. The findings highlight significant spatial and temporal variations in NDVI, with an annual increase averaging 0.00197 per year (p < 0.0001). This positive trend is coupled with an increase in precipitation by 0.4801 mm/year (p = 0.0016). In contrast, our analysis recorded a slight decrease in temperature (− 0.01011 °C/year, p < 0.05) and a reduction in solar radiation (− 0.27526 W/m2/year, p < 0.05). Notably, cross-wavelet transform analysis underscored significant coherence between NDVI and climatic factors, revealing periods of synchronized fluctuations and distinct lagged relationships. This analysis particularly highlighted precipitation as a primary driver of vegetation growth, illustrating its crucial impact across various Pakistani regions. Moreover, the analysis revealed distinct seasonal patterns, indicating that vegetation health is most responsive during the monsoon season, correlating strongly with peaks in seasonal precipitation. Our investigation has revealed Pakistan's complex association between vegetation health and climatic factors, which varies across different regions. Through cross-wavelet analysis, we have identified distinct coherence and phase relationships that highlight the critical influence of climatic drivers on vegetation patterns. These insights are crucial for developing regional climate adaptation strategies and informing sustainable agricultural and environmental management practices in the face of ongoing climatic changes.

Magnetogravimetric study on the Scotia Plate, in the South Atlantic Ocean for the characterization of tsunamis

The marine and coastal environments of the Scotia Sea regions in the Southern Atlantic Ocean and Antarctica are vulnerable to the potentially disastrous effects of seismic activity along the Scotia Arc. This paper presents a magnetogravimetric study of the Scotia Plate for tsunami characterization. The influence of earthquakes on the Geomagnetic Field (GMF) is investigated using data from INTERMAGNET network observatories. A tectonic model is evaluated using gravity data from NOAA and seismic refraction data from Lamont-Doherty Earth Observatory. The study also assesses the impact on water level (WL) measured at Intergovernmental Oceanographic Commission (IOC) tide gauge stations. Cross Wavelet Transform (XWT) is applied, and a frequency analysis of the GMF is conducted to identify specific frequencies during seismic events. A 2D tectonic model is constructed for the North Scotia Ridge using gravimetric and seismic data to characterize structural boundaries that may be activated during seismic events. Water level records collected from 6 tide gauge stations in the region are filtered and analyzed to identify tsunamis at each station. The results reveal anomalous frequencies in the frequency analysis of the horizontal component of the GMF during the November 25, 2013 earthquake, with high data correlation from different observatories in the study area for periods of 0.5 and 1 hour. Gravimetric modeling delineates faults activated during seismic activity and edges of structures potentially activated due to the transcurrent and compressional nature of the margin. WL anomalies up to 1.30 m are obtained following earthquakes with a magnitude greater than 8. The propagation speed in the study area averaged 460 km/h, consistent with the expected speed for those depths, except for Puerto Argentino, which exceeded them in 50%.

The relationship between Chinese and FOB prices of rare earth elements – Evidence in the time and frequency domain

This paper investigates the relationship between domestic and export prices of rare earth elements (REEs) taking into account both, the time and the frequency domain using Granger causality tests and cross wavelet analysis. We find areas of significant comovement between both, returns and volatility of domestic and export prices, with the degree of connection being low at higher frequencies, increasing over medium frequencies and lower frequencies. Moreover, the lead-lag effects between the Chinese and the export market are time-varying and heterogenous, with no particular market as the leader. The analysis of volatility transmission shows that own-market volatility rather than spillover from the Chinese to the export market or vice versa accounts for the biggest share of volatility. While volatility transmission shows substantial variation over time, the Chinese market is generally a net giver of volatility to the export market, especially at the medium frequency band. Accordingly, market participants buying REEs in the export market might want to track Chinese prices as leading indicators of price fluctutations due to spillovers.

Middle to late Holocene hydroclimate variability revealed by multi-proxy stalagmite records from Jinfo Cave, central-west China

The pattern and cause of middle-to late-Holocene hydroclimatic variability in central-west China remain unclear. We present a new pair of stalagmite isotopic (δ18O and δ13C) records and trace element ratio (Mg/Ca, Ba/Ca, and Sr/Ca) records from Jinfo Cave, central-west China. We established a uranium-series chronology that shows that the stalagmite grew between 5.6 and 0.6 kyr BP. The similarities between our δ18O stalagmite record and other published records suggest that δ18O generally reflects overall monsoon intensity or rained-out effect between the cave site and moisture source, but not necessarily regional rainfall amount. Corresponding changes in Mg/Ca, Ba/Ca, Sr/Ca, and δ13C are mostly dependent on local factors, such as the nature of the ecosystem above the cave, water-rock interactions, and the prior calcite precipitation effect. As the δ13C records bear a remarkable resemblance to Mg/Ca, Ba/Ca, and Sr/Ca records, we extracted the first principal component (PC1) of the four records and identified the PC1 as a regional hydroclimate proxy. The bandpass filter and cross wavelet transformation results show that the ∼500 years periodicity of PC1 exhibits in-phase oscillations with solar activity and the El Niño-Southern Oscillation (ENSO) throughout the middle to late Holocene, suggesting that changes in solar activity influence centennial hydroclimate variation and are amplified by ENSO. Another prominent feature seen in PC1 is a progressive positive shift from 3.0 kyr BP, which indicates that central-west China moved toward a drier climate over this period. We attribute this climate transition to a strengthening El Niño event and its impact on oceanic and atmospheric anomalies.

Temporal and Spatial Evolution of Meteorological Drought in Inner Mongolia Inland River Basin and Its Driving Factors

In order to analyze the temporal and spatial evolution of meteorological drought and explore its driving factors, the inland river basin of Inner Mongolia (IMIRB) was taken as a typical research area, the Standardized Precipitation Evapotranspiration Index (SPEI) of various scales was calculated, and the spatio-temporal trend change characteristics of meteorological drought were analyzed combined with the modified Mann–Kendall trend test (MMK). The typical meteorological drought events were analyzed by using the three-dimensional identification method, and the spatio-temporal evolution characteristics and dynamic evolution law of meteorological drought were analyzed comprehensively and accurately. The driving effects of Pacific Decadal Oscillation (PDO), North Atlantic Multidecadal Oscillation (AMO), Arctic Oscillation (AO), El Niño-Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), and sunspot on meteorological drought were investigated by using the cross wavelet method. The results are as follows: (1) with the increase of SPEI time scale, the frequency of meteorological drought decreased, but the duration and intensity of drought increased; (2) the trend was greatest in spring, with the largest number of areas showing a significant downward trend in SPEI, the strongest persistence in intensity, and significant aridification characteristics; (3) summer meteorological droughts had the largest area of high intensity drought but the smallest area of high frequency areas, and winter droughts had the smallest area of high intensity drought but the largest percentage of high frequency areas; (4) the meteorological drought event that occurred from April 2017 to December 2017 was the most serious, and reached its maximum value in June 2017, which mainly experienced five processes: occurrence—intensification—attenuation—re-intensification—extinction; (5) atmospheric circulation factor, sunspot, and meteorological drought of IMIRB were correlated, and ENSO had the greatest effect on drought. This study provides effective theoretical support for IMIRB drought prevention and disaster reduction.

Bivariate Assessment of Hydrological Drought of a Semi-Arid Basin and Investigation of Drought Propagation Using a Novel Cross Wavelet Transform Based Technique

Bivariate Assessment of Hydrological Drought of a Semi-Arid Basin and Investigation of Drought Propagation Using a Novel Cross Wavelet Transform Based Technique

Robust Low Complexity Framework for Early Diagnosis of Autism Spectrum Disorder Based on Cross Wavelet Transform and Deep Transfer Learning

Robust Low Complexity Framework for Early Diagnosis of Autism Spectrum Disorder Based on Cross Wavelet Transform and Deep Transfer Learning

Decoding the government budget puzzle

Purpose: The relationship between public expenditure and revenue that determines the budget balance in the explanation of budget deficits is very important. This study aimed to examine the association between public spending and income in Turkey. Methodology: This article applies the continuous wavelet transform method (CWT) to study the relation-ship between government revenues and expenditures in Turkey. The study, which covers the period 2006-2020, consists of monthly data. The following four CWT tools were used in the analysis: Wavelet Power Spectrum, Cross Wavelet Power, Wavelet Coherence, and Phase Difference. Results: The results of the study, controlling taxes rather than expenditures, play a key role in reducing fis-cal deficits in Turkey in the short term. It is revealed that the dynamics of the budget balance in the medium term can be explained by the spend-tax hypothesis and that the tax burden can be alleviated by spending cuts. In the long term, results were obtained that support the spend-tax hypothesis. Conclusion: The paper reveals that taxes play an important role in controlling budget deficits in the short run. It turns out that the dynamics of the budget balance can be explained in the medium term by expendi-ture cuts and then the tax burden can be alleviated. In the long run, implementing fiscal policies based on public spending rather than taxation will be more effective against budget deficits.

Long-term spatiotemporal characteristics of meteorological drought in China from a three-dimensional (longitude, latitude, time) perspective

Currently, there is an absence of an exhaustive elucidation regarding the long-term spatiotemporal characteristics of meteorological droughts in China. Previous studies have predominantly fixated on the lower-dimensional (temporal or spatial) facets of drought events, overlooking critical spatiotemporal details. This paper proposes an integrated processing framework that employs a three-dimensional clustering algorithm to discern drought events and quantifies the spatiotemporal patterns and dynamic attributes of China’s meteorological droughts during 1950–2021 from a three-dimensional perspective encompassing longitude, latitude, and time, capturing comprehensive spatiotemporal information. Additionally, the cross wavelet transform is harnessed to elucidate the impact of teleconnection factors on meteorological drought. The findings underscore that prolonged and extensive meteorological drought events, characterized by heightened severity, primarily manifest during the 1950 s, 1960 s, and 2010 s in temporal, predominantly within the central and western China spatially. From 1950 onwards, the severity, duration, and affected area of meteorological droughts in China exhibit a discernible pattern of initial decline followed by escalation. Into the 2010 s, meteorological droughts became more severe and active. China's meteorological drought displays two relatively prevailing spatial migration patterns: an overarching eastward and westward migration. In the former, the most frequent migration occurs to the east and northeast during its monthly evolution, while the latter involves migration primarily towards the west, southwest, and northwest. The impact of teleconnection factors on meteorological drought shows a spatiotemporal heterogeneity, with ENSO exhibiting a relatively substantial and intricate influencer. This research unearths the latest long-term spatiotemporal dynamics of meteorological drought in China, which contributes to an improved comprehension of drought patterns and behaviors.