Variability Of Monsoon Rainfall Research Articles

Optimization of CMIP6 models for simulation of summer monsoon rainfall over India by analysis of variance

AbstractThe advent of weather and climate models has equipped us to forecast or project monsoon rainfall patterns over various spatiotemporal scales; however, utilizing a single model is not usually sufficient to yield accurate projection due to the inherent uncertainties associated with the individual models. An ensemble of models or model runs is often used for better projections as a multimodel ensemble (MME). This study analyzes the accuracy of MME in simulating the Indian summer monsoon rainfall (ISMR) variability using Coupled Model Intercomparison Project Phase 6 (CMIP6) simulations. The results highlighted that although the MME primarily reproduces the observed pattern and annual cycle of rainfall, significant biases are noted over homogeneous meteorological regions of India, except northeast India. To overcome this issue, an analysis of variance (ANOVA) and post hoc statistical tests are employed to identify a group of models for which the modified MME gives a better estimate of rainfall and reduces the bias significantly. Our findings underscore the potential of ANOVA and post hoc tests as a practical approach to enhancing the accuracy of multimodel ensemble rainfall for the assessment of model projections.

Future Climate Change Impacts on Rice in Uttar Pradesh, India's Most Populous Agrarian State

AbstractUttar Pradesh, with a population of 237 million, is the largest agrarian state in India, located in the Indo‐Gangetic plains. Rice cultivation is widespread across all districts of Uttar Pradesh, which have varying climate regimes, irrigation infrastructures, crop management practices, and farm sizes. The state is characterized by different agroecological zones (AEZs) with semi‐arid to sub‐humid climates with significant variability in monsoon rainfall. In this study, the impact of climate change on Kharif‐season rice is estimated using crop‐climate scenarios in Uttar Pradesh. A process‐based Crop Simulation Model, Crop Estimation through Resource and Environment Synthesis‐Rice, was simulated with bias‐corrected and downscaled climate data for historical (1995–2014) and three future periods (the 2030s, 2050s, and 2090s) for two mitigation pathways (SSP2‐4.5 and SSP5‐8.5) from the Coupled Model Intercomparison Project 6. Phenology, irrigation amount, crop evapotranspiration, yield, and water use efficiency were evaluated and assessed for all AEZs. Based on the ensemble of 16 climate models, rainfed rice yield increased in the AEZs of western Uttar Pradesh due to increased rainfall, while in eastern Uttar Pradesh yield decreased, under both shared socioeconomic pathways (SSPs). Irrigated rice yield decreased in all AEZs under both SSPs due to an increase in temperature and a decrease in the length of the growing period, with reductions of up to 20% by the 2090s. Irrigation requirements decreased from the 2030s to the 2090s due to increased rainfall and decreased crop evapotranspiration. Despite the projected increase in rainfed yield, the overall rice yield is expected to decrease in the future under both SSPs.

Analysis of the onset, withdrawal, and variability of monsoon rainfall in the face of climate change

Climate change is affecting the precipitation system in Bangladesh, particularly in the northwestern hydrological zone. This study analyzed the monsoon rainfall variability from 1991 to 2015, revealing a shifting pattern and past trends. This study used the Mann–Kendall test and Sen’s slope (MAKESENS) statistical model to evaluate the annual total rainfall and monsoon rainfall. Approximately 71–78% of the total rainfall occurred during the monsoon season at all stations, with Rangpur being the rainiest and Rajshahi being the driest. The annual rainfall trend decreased, with highly variable arrival and withdrawal days. The monsoon arrived early by 2 to 39 days and late by 1 to 72 days from the reference day. The withdrawal of monsoon rainfall occurred 1 to 38 days early and 1 to 39 days late. These findings can be used to plan and manage agricultural practices at the local level in northwestern Bangladesh.

Unusual subseasonal variability of Indian summer monsoon rainfall in 2020

AbstractAbove‐normal all‐India summer monsoon (ISM) rainfall was received in 2020 with 109% of its long‐period average. According to the India Meteorological Department (IMD), ISM 2020 experienced robust month‐to‐month rainfall variations; in particular, the peak monsoon month of July 2020 witnessed deficit rainfall. The present study investigated the possible mechanisms that are responsible for the July 2020 deficit rainfall. Analysis revealed that the strong low‐level moisture divergence caused by the westward‐propagating atmospheric Rossby wave, induced by suppressed western North Pacific (WNP) convection, is primarily responsible for the July rainfall deficit. The WNP suppressed convection is linked to anomalous low‐level anticyclonic circulation. It is noted that the intense WNP anticyclone is maintained by the tropical Indian Ocean (TIO) warming‐induced atmospheric Kelvin waves and strong low‐level convergence over the Meiyu‐Baiu rainband. Unlike July, the strength of the WNP anticyclone and TIO warming are weaker in June and August. In addition to the observational analysis, the skill of the Climate Forecast System version‐2 (CFSv2) model in predicting the July rainfall at a lead of about two months is examined. Strong positive rainfall anomalies over India are predicted by the model in July, which is in contrast to the observations. Despite predicting the TIO warming, the model failed to capture the westward extension of the WNP anticyclone, which impacted the July rainfall prediction. To understand the importance of the WNP anticyclone on ISM rainfall, a CFSv2 sensitivity experiment is carried out by imposing strong negative sea surface temperature (SST) anomalies over the WNP region to excite the anticyclone. Both the westward extension of the WNP anticyclone and suppressed rainfall over the core monsoon region in July are captured by the model when low‐level circulation is imposed. The model sensitivity experiment strongly supports the role of the WNP anticyclone and its westward extension in inducing reduced rainfall in July 2020 over the monsoon trough region.

Monsoon Mission Coupled Forecast System version 2.0: model description and Indian monsoon simulations

Abstract. We present the Monsoon Mission Coupled Forecast System version 2.0 (MMCFSv2) model, which substantially upgrades the present operational MMCFSv1 (version 1) at the India Meteorology Department. The latest 25 years (1998–2022) of retrospective seasonal coupled hindcast simulations of the Indian summer monsoon with April initial conditions from Coupled Forecast System Reanalysis are discussed. MMCFSv2 simulates the tropical wind, rainfall, and temperature structure reasonably well. MMCFSv2 captures surface winds well and reduces precipitation biases over land, except over India and North America. The dry bias over these regions remained like in MMCFSv1. MMCFSv2 captures significant features of the Indian monsoon, including the intensity and location of the maximum precipitation centers and the large-scale monsoon circulation. MMCFSv2 improves the phase skill (anomaly correlation coefficient) of the interannual variation of Indian summer monsoon rainfall (ISMR) by 17 % and enhances the amplitude skill (normalized root mean square error) by 20 %. MMCFSv2 shows improved teleconnections of ISMR with the equatorial Indian and Pacific oceans. This 25-year hindcast dataset will serve as the baseline for future sensitivity studies of MMCFSv2.

Climate Change Impact on the Trigger of Natural Disasters over South-Eastern Himalayas Foothill Region of Myanmar: Extreme Rainfall Analysis

The study examines the characteristics and variability of monsoon rainfall in Myanmar, focusing on the relationship between heavy rainfall, floods, and earthquakes, which impact agriculture, hydrology, and the environment. Generally, heavy rainfall can cause flooding, economic losses, and water table changes. Northern Myanmar floods occur mainly during the monsoon season from June to October and can be classified into widespread floods along major rivers like Ayeyarwady, Thanlwin, Chindwin, and Sittoung and flash floods in small streams and rivers. Climate change is expected to increase the frequency and intensity of extreme weather events, including heavy rainfall, which can trigger floods or landslides, which also can in turn cause earthquakes. Heavy rainfall over northern Myanmar and the Sagaing faults, which are the main triggers of earthquakes, has been the subject of several studies. The study uses the Copernicus 5 database of global climate model (GCM) simulations with two scenario analyses on climate change detection and indices (ETCCDI) to study changes in climatic extremes. Results show high intensity in the northern region and monsoon core regions, while the central region shows less intensity. The study also uses intensity-duration-frequency (IDF) curves to analyze the relationship between rainfall duration, intensity, and return time in major risk zones. The study finds that as short duration lengthens, rainfall intensity increases for future rainfall patterns. This information is expected to be convenient for local authorities and flood protection projects in rural and urban basins.

Orbitally forced and internal changes in West African rainfall interannual-to-decadal variability for the last 6000 years

Recent variability in West African monsoon rainfall (WAMR) has been shown to be influenced by multiple ocean–atmosphere modes, including the El Niño Southern Oscillation, Atlantic Multidecadal Oscillation and the Interdecadal Pacific Oscillation. How these modes will change in response to long term forcing is less well understood. Here we use four transient simulations driven by changes in orbital forcing and greenhouse gas concentrations over the past 6000 years to examine the relationship between West African monsoon rainfall multiscale variability and changes in the modes associated with this variability. All four models show a near linear decline in monsoon rainfall over the past 6000 years in response to the gradual weakening of the interhemispheric gradient in sea surface temperatures. The only indices that show a long-term trend are those associated with the strengthening of the El Niño Southern Oscillation from the mid-Holocene onwards. At the interannual-to-decadal timescale, WAMR variability is largely influenced by Pacific–Atlantic – Mediterranean Sea teleconnections in all simulations; the exact configurations are model sensitive. The WAMR interannual-to-decadal variability depicts marked multi-centennial oscillations, with La Niña/negative Pacific Decadal Oscillation and a weakening and/or poleward shift of subtropical high-pressure systems over the Atlantic favoring wet WAMR anomalies. The WAMR interannual-to-decadal variability also depicts an overall decreasing trend throughout the Holocene that is consistent among the simulations. This decreasing trend relates to changes in the North Atlantic and Gulf of Guinea Sea Surface Temperature variability.

Spatio-Temporal Variability of Rainfall Over Kathmandu Valley of Nepal

The present study used and analyzed rainfall data from 18 meteorological stations from 1971 to 2013 to examine the spatial and temporal variability of seasonal and annual rainfall based on rain gauge measurements. The monthly to annual rainfall analysis was carried out for each site of Kathmandu Valley. Rainfall amounts in Kathmandu Valley vary considerably in space and time. The minimum mean monthly rainfall is observed in November which is 6.5 mm and maximums of 447.8 mm in July. Monsoon is the main contributor of the rainfall which is 80% followed by pre-monsoon with 13.6%, post-monsoon with 3.6% and winter with 2.8%. Spatial interpolation was used to explore spatial variability of seasonal and annual rainfall over Kathmandu Valley. There is large spatial variability of monsoon rainfall; generally, the upper parts of the Kathmandu Valley received the heavy monsoon rainfall than the lower parts of the Valley floor. The rainfall of Kathmandu has marked decreasing in recent a couple of decades. Annual rainfall has decreased by 0.96 mm/year observed in Kathmandu Valley.

Assessing seasonal variation and trends in rainfall patterns of Madhya Pradesh, Central India

Abstract Climate change is a worldwide problem caused by various anthropogenic activities, leading to changes in hydroclimatic variables like temperature, rainfall, riverine flow, and extreme hydrometeorological events. In India, significant changes are noted in its natural resources and agriculture sectors. In this study, we analysed the long-term spatio-temporal change in rainfall patterns of Madhya Pradesh, Central India, using Indian Meteorological Department high-resolution gridded data from 439 grid points. The coefficient of variance analysis showed low variability in annual and monsoon rainfall but significant variability in pre-monsoon, post-monsoon, and winter seasons, indicating considerable seasonal variation. Pre-monsoon rainfall exhibited an increasing trend (0.018 mm annually), while annual, monsoon, post-monsoon, and winter rainfall showed decreasing trends. Change point analysis identified shifts in rainfall patterns in 1998 (monsoon, annual), 1955 (pre-monsoon), 1987 (post-monsoon), and 1986 (winter). Spatio-temporal distribution maps depicted irregular rainfall, with some areas experiencing drastic declines in precipitation after 1998. The maximum average annual rainfall reduced from 1,769 to 1,401 mm after 1998 affecting water availability. The study's findings highlight a significant shift in Madhya Pradesh's seasonal rainfall distribution after 1998, urging researchers and policymakers to address water-intensive cropping practices and foster climate resilience for a sustainable future in the region.

Evaluation of MERRA-2 Black Carbon Characteristics and Potential Sources over China

Black carbon (BC), an important component of atmospheric aerosol, plays a significant role in regional climate, hydrological cycle, variation of monsoon rainfall, and human health. The 40-year detailed atmospheric BC over China from 1981 to 2020 is systematically investigated through the MERRA-2 reanalysis dataset. MERRA-2 BC generally showed a good correlation (R = 0.68) compared with 673 monthly samples from ground-based observation at 35 stations around China. The overall annual average of MERRA-2 BC concentration over China is 1.15 μg/m3, with a fast growth rate during 1981–2007 and a relatively slow decrease after that. The winter season has the highest mean concentration of BC, followed by autumn and spring, whereas summer shows relatively weaker values. The order of annual average BC concentrations during 1981–2020 is Beijing-Tianjin-Hebei region (BTH, 4.02 μg/m3) > Sichuan Basin (SB, 3.94 μg/m3) > Yangtze River Delta (YRD, 2.68 μg/m3) > Pearl River Delta (PRD, 1.47 μg/m3). The monthly mean BC concentrations over the BTH, YRD, PRD and SB are estimated to be smallest 3.18 μg/m3 in May, 1.94 μg/m3 in August, 0.82 μg/m3 in July, 3.04 μg/m3 in June, respectively, whilst largest consistently in December with 5.09 μg/m3, 3.83 μg/m3, 2.12 μg/m3, and 5.41 μg/m3, respectively. Our study indicates the primary potential source areas for BC are concentrated in the research city and its surroundings. Beijing and Chengdu are more BC-polluted areas than Shanghai and Guangzhou. Long-distance, regional transfer from south BTH contributes importantly to BC pollution in Beijing under the influence of prevailing southerly winds. The geographical location of Chengdu causes the transport and accumulation of BC inside the SB.

Weakening of decadal variation of Northern Hemisphere land monsoon rainfall under global warming

Over the past century, Northern Hemisphere (NH) land monsoon rainfall (NHLMR) experienced significant decadal to multidecadal variations, mainly driven by an east–west sea surface temperature (SST) contrast over the Pacific (EWPC) and an interhemispheric North Atlantic–South Indian Ocean SST dipole (NAID). However, how the NHLMR’s decadal variation would vary and whether the oceanic forcing could continue to drive it in a warming world remain unexplored. Here, by analyzing 24 Coupled Model Intercomparison Project Phase 6 (CMIP6) models’ historical simulations and future projections, we show that the leading mode of decadal NHLMR will retain its nearly-uniform spatial pattern and representation of the NHLMR’s intensity. In the future, the significant periodicities of decadal NHLMR are shortened as emissions levels increase. The intensity of decadal NHLMR variation will experience a comprehensive decline under various emission scenarios, which may link to the weakened intensity of NAID and EWPC. Although the relationship between EWPC and decadal NHLMR is slightly weakened in the future, EWPC will remain a primary driver while NAID is no longer. The significant historical correlation between NAID and NHLMR is mainly attributed to the influence of increased anthropogenic aerosols emission. However, the NAID-NHLMR linkage would no longer exist owing to the decline of anthropogenic aerosol emission in the future.

Regional and temporal variability of Indian summer monsoon rainfall in relation to El Niño southern oscillation

The Indian summer monsoon rainfall (ISMR) exhibits significant variability, affecting the food and water security of the densely populated Indian subcontinent. The two dominant spatial modes of ISMR variability are associated with the El Niño Southern Oscillation (ENSO) and the strength of the semi-permanent monsoon trough along with related variability in monsoon depressions, respectively. Although the robust teleconnection between ENSO and ISMR has been well established for several decades, the major drivers leading to the time-varying relationship between ENSO and ISMR patterns across different regions of the country are not well understood. Our analysis shows a consistent increase from a moderate to substantially strong teleconnection strength between ENSO and ISMR from 1901 to 1940. This strengthened relationship remained stable and strong between 1941 and 1980. However, in the recent period from 1981 to 2018 the teleconnection decreased consistently again to a moderate strength. We find that the ENSO–ISMR relationship exhibits distinct regional variability with time-varying relationship over the north, central, and south India. Specifically, the teleconnection displays an increasing relationship for north India, a decreasing relationship for central India and a consistent relationship for south India. Warm SST anomalies over the eastern Pacific Ocean correspond to an overall decrease in the ISMR, while warm SST anomalies over the Indian Ocean corresponds to a decrease in rainfall over the north and increase over the south of India. The central Indian region experienced the most substantial variation in the ENSO–ISMR relationship. This variation corresponds to the variability of the monsoon trough and depressions, strongly influenced by the Pacific Decadal Oscillation and North Atlantic Oscillation, which regulate the relative dominance of the two spatial modes of ISMR. By applying the PCA-Biplot technique, our study highlights the significant impacts of various climate drivers on the two dominant spatial modes of ISMR which account for the evolving nature of the ENSO–ISMR relationship.

Spatiotemporal Rainfall Variability and Trends over the Mahi Basin, India

Climate change can have an influence on rainfall that significantly affects the magnitude frequency of floods and droughts. Therefore, the analysis of the spatiotemporal distribution, variability, and trends of rainfall over the Mahi Basin in India is an important objective of the present work. Accordingly, a serial autocorrelation, coefficient of variation, Mann–Kendall (MK) and Sen’s slope test, innovative trend analysis (ITA), and Pettitt’s test were used in the rainfall analysis. The outcomes were derived from the monthly precipitation data (1901–2012) of 14 meteorology stations in the Mahi Basin. The serial autocorrelation results showed that there is no autocorrelation in the data series. The rainfall statistics denoted that the Mahi Basin receives 94.8% of its rainfall (821 mm) in the monsoon period (June–September). The normalized accumulated departure from the mean reveals that the annual and monsoon rainfall of the Mahi Basin were below average from 1901 to 1930 and above average from 1930 to 1990, followed by a period of fluctuating conditions. Annual and monsoon rainfall variations increase in the lower catchment of the basin. The annual and monsoon rainfall trend analysis specified a significant declining tendency for four stations and an increasing tendency for 3 stations, respectively. A significant declining trend in winter rainfall was observed for 9 stations under review. Likewise, out of 14 stations, 9 stations denote a significant decrease in pre-monsoon rainfall. Nevertheless, there is no significant increasing or decreasing tendency in annual, monsoon, and post-monsoon rainfall in the Mahi Basin. The Mann–Kendall test and innovative trend analysis indicate identical tendencies of annual and seasonal rainfall on the basin scale. The annual and monsoon rainfall of the basin showed a positive shift in rainfall after 1926. The rainfall analysis confirms that despite spatiotemporal variations in rainfall, there are no significant positive or negative trends of annual and monsoon rainfall on the basin scale. It suggests that the Mahi Basin received average rainfall (867 mm) annually and in the monsoon season (821 mm) from 1901 to 2012, except for a few years of high and low rainfall. Therefore, this study is important for flood and drought management, agriculture, and water management in the Mahi Basin.

Variations in Indian Summer Monsoon Rainfall patterns in Changing Climate

The Indian summer monsoon is a large scale synoptic and dominant circulation feature which is largely restricted to the summer months from June to September. The proper understanding of rainfall pattern and its trends may help water resources development, agriculture sector and to take decisions for developmental activities. The present study is an attempt to evaluate the spatial variability in Indian summer monsoon rainfall (ISMR) over the Indian subcontinent during the climatological period (1901-2010). The long-term annual, decadal and tricadal monsoon rainfall differences are considered for the period 1901-2010 during the monsoon (June-September) and peak monsoon month (July-August). The results show concern for the major areas of upper Himalaya, Western and peninsular India where positive rainfall difference/increase rainfall with 0.2 to 1 mm/day variation have been reported during the monsoon and peak monsoon months. Also, decrease in rainfall have been reported over Western Ghats, Indo-Gangetic Plain (IGP) and some central Indian regions in the range of -0.6 to -1.5 mm/day. Further, a broad overview of the study shows an enhancement of ISMR over Western India whereas a substantial decline over Northeast Indian regions which suggests the western shift of ISMR in changing climate.

Glacial changes in sea level modulated millennial-scale variability of Southeast Asian autumn monsoon rainfall

Most paleoclimate studies of Mainland Southeast Asia hydroclimate focus on the summer monsoon, with few studies investigating rainfall in other seasons. Here, we present a multiproxy stalagmite record (45,000 to 4,000years) from central Vietnam, a region that receives most of its annual rainfall in autumn (September-November). We find evidence of a prolonged dry period spanning the last glacial maximum that is punctuated by an abrupt shift to wetter conditions during the deglaciation at ~14 ka. Paired with climate model simulations, we show that sea-level change drives autumn monsoon rainfall variability on glacial-orbital timescales. Consistent with the dry signal in the stalagmite record, climate model simulations reveal that lower glacial sea level exposes land in the Gulf of Tonkin and along the South China Shelf, reducing convection and moisture delivery to central Vietnam. When sea level rises and these landmasses flood at ~14 ka, moisture delivery to central Vietnam increases, causing an abrupt shift from dry to wet conditions. On millennial timescales, we find signatures of well-known Heinrich Stadials (HS) (dry conditions) and Dansgaard-Oeschger Events (wet conditions). Model simulations show that during the dry HS, changes in sea surface temperature related to meltwater forcing cause the formation of an anomalous anticyclone in the Western Pacific, which advects dry air across central Vietnam, decreasing autumn rainfall. Notably, sea level modulates the magnitude of millennial-scale dry and wet phases by muting dry events and enhancing wet events during periods of low sea level, highlighting the importance of this mechanism to autumn monsoon variability.

Variability and predictability of the Northeast India summer monsoon rainfall

AbstractThe mesoscale orography over one of the wettest regions of the world makes the Northeast India (NEI) vulnerable to hydrological disasters while sustaining a biodiversity “hotspot.” The monsoon rainy season over the NEI is known to be longer than June–September (JJAS), but an objective definition has been lacking. Understanding the drivers and predictability of rainfall variability over the region is key for sustainable development planning and adaptation to increasing disasters in the backdrop of a warming climate but remained poor due to lack of a working definition of “summer monsoon season” over NEI. Here, using the relationship between rainfall over the region and winds at 850 hPa over North Bay of Bengal (BoB), we provide an objective definition of the monsoon season over NEI and argue that the “summer monsoon rainy season” over the NEI (NEIR) is from May to September (MJJAS). In contrast to a significant negative relationship between JJAS Central India (CI) rainfall and a JJAS El Niño–Southern Oscillation (ENSO) index, the MJJAS rainfall over the NEI has no relationship with MJJAS or JJAS ENSO. Instead, we unravel that (a) the tropical Northwest‐Pacific (TNWP) sea surface temperature (SST), (b) the South Equatorial Indian Ocean Dipole (SEIOD) and (c) the Atlantic Zonal mode (AZM) are potential drivers of the NEIR variability. Using a causal inference algorithm, namely the Peter and Clark Momentary Conditional Independence (PCMCI) method, we show that SEIOD directly influences NEIR while TNWP SST has a two‐way connection with the NEIR. While “internal variability” may be higher over the NEI, significant modulation of the variances of the subseasonal fluctuations by predictable drivers like the Atlantic Niño and North Atlantic water temperature provides optimism for seasonal prediction of the NEIR.

Latitudinal variations of summer monsoon rainfall in different intensity classes over the Western Ghats

Latitudinal variations of summer monsoon rainfall in different intensity classes over the Western Ghats

Assessment of intraseasonal variability indices on identifying Indian summer monsoon rainfall variability

Assessment of intraseasonal variability indices on identifying Indian summer monsoon rainfall variability

Statistical Analysis of Rainfall Pattern and Trend of Chhattisgarh State, India

To analyze, the present rainfall status and trend, the long term weather data (1980-2020) viz., gridded annual rainfall data was analysed which was obtained from the department of Agrometeorology, Indira Gandhi Krishi Vishwavidyalaya Raipur, Using linear trend, the long-term change in rainfall has been evaluated. It is obsered that the state receives the maximum amount of rainfall from the southwest monsoon, around 87 percent from June to September, the state receives 15 percent in June and the state receives 29,26 and 16 percent rainfall in the Month of July, August and September, respectively. Long term weather data of state rainfall, it is seen that rainfall in June and September is less consistent since the coefficient of variation is around 33 and 30 percent, respectively; we would conclude that rainfall in June has more variability than in monsoon months, SW monsoon and annual rainfall. Average Monsoon and Annual rainfall variability are less than monsoon months. District-wise minimum rainfall variability of June, July, August, and September is around 37 percent (Kanker) and 22 percent (Jashpur). Kabirdham district has a 25 percent variability in August and September. District Kanker and Raigarh varied around 14 percent for monsoon and annual rainfall. As the linear regression in southwest monsoon and annual rainfall in the state of Chhattisgarh is increasing in order, but from 1980 to 2020 it shows more fluctuation in trend line, then if a system is developed for agrometeorology station at cluster level or block level and for forecasting, it will be able to account for these changes.From this, farmers will receive timely information about the monsoon and rainfall, allowing them to conduct agricultural activities at the appropriate time.

Eccentricity-paced geomagnetic field and monsoon rainfall variations over the last 870 kyr

Whether there are links between geomagnetic field and Earth's orbital parameters remains unclear. Synchronous reconstructions of parallel long-term quantitative geomagnetic field and climate change records are rare. Here, we present 10Be-derived changes of both geomagnetic field and Asian monsoon (AM) rainfall over the last 870kyr from the Xifeng loess-paleosol sequence on the central Chinese Loess Plateau. The10BeGM flux (a proxy for geomagnetic field-induced 10Be production rate) reveals 13consecutive geomagnetic excursions in the Brunhes chron, which are synchronized with the global records, providing key time markers for Chinese loess-paleosol sequences. The10Be-derived rainfall exhibits distinct ~100 kyr glacial-interglacial cycles, and superimposed precessional (~23 kyr) cycles that match with those in Chinese speleothem δ18O record. We find that changes in the geomagnetic field and AM rainfall share a common ~100 kyr cyclicity, implying a likely eccentricity modulation of both the geomagnetic field and climate.