Projected Changes In Precipitation Research Articles

Future climate projection across Tanzania under CMIP6 with high-resolution regional climate model

Climate change is one of the most pressing challenges faced by developing countries due to their lower adaptive capacity, with far-reaching impacts on agriculture. The mid-century period is widely regarded as a critical moment, during which adaptation is deemed essential to mitigating the associated impacts. This study presents future climate projections across Tanzania using the latest generation of global climate models (CMIP6) combined with a high-resolution regional climate model. The findings indicate that, the trends in temperature and precipitation in Tanzania from 1991 to 2020, minimum temperatures showed the highest variability with a trend of 0.3 °C, indicating significant fluctuations in minimum temperature over the decades. Maximum temperatures also showed high variability with a trend of 0.4 °C. There is a range of variability in precipitation per decade for different regions in Tanzania, with some regions experiencing significant decreases in precipitation of up to − 90.3 mm and − 127.6 mm. However, there were also regions that experienced increases in precipitation, although these increases were generally less than 4.8 mm over the decades. The projections of minimum and maximum temperatures from 2040 to 2071 under the Shared Socioeconomic Pathways (SSP) 2–4.5 and SSP 5–8.5 are projected to increase by 0.14 °C to 0.21 °C per decade, across different regions. The average projected precipitation changes per decade vary across regions. Some regions are projected to experience increases in precipitation. Other regions are projected to show decreases in precipitation within the range of − 0.6 mm to 15.5 mm and − 1.5 mm to 47.4 mm under SSP2–4.5 and SSP5–8.5 respectively. Overall, both scenarios show an increase in projected temperatures and precipitation for most regions in Tanzania, with some areas experiencing more significant increases compared to others. The changes in temperatures and precipitation are expected to have significant impacts on agriculture and water resources in Tanzania.

Spatiotemporal changes in future precipitation of Afghanistan for shared socioeconomic pathways

Global warming induces spatially heterogeneous changes in precipitation patterns, highlighting the need to assess these changes at regional scales. This assessment is particularly critical for Afghanistan, where agriculture serves as the primary livelihood for the population. New global climate model (GCM) simulations have recently been released for the recently established shared socioeconomic pathways (SSPs). This requires evaluating projected precipitation changes under these new scenarios and subsequent policy updates. This research employed six GCMs from the CMIP6 to project spatial and temporal precipitation changes across Afghanistan under all SSPs, including SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. The employed GCMs were bias-corrected using the Global Precipitation Climatological Center's (GPCC) monthly gridded precipitation data with a 1.0° spatial resolution. Subsequently, the climate change factor was calculated to assess precipitation changes for both the near future (2020–2059) and the distant future (2060–2099). The bias-corrected projections' multi-model ensemble (MME) revealed increased precipitation across most of Afghanistan for SSPs with higher emissions scenarios. The bias-corrected simulations showed a substantial increase in summer precipitation of around 50%, projected under SSP1-1.9 in the southwestern region, while a decline of over 50% is projected in the northwestern region until 2100. The annual precipitation in the northwest region was projected to increase up to 15% for SSP1-2.6. SSP2-4.5 showed a projected annual precipitation increase of around 20% in the southwestern and certain eastern regions in the far future. Furthermore, a substantial rise of approximately 50% in summer precipitation under SSP3-7.0 is expected in the central and western regions in the far future. However, it is crucial to note that the projected changes exhibit considerable uncertainty among different GCMs.

Modeling the impact of future rainfall changes on the effectiveness of urban stormwater control measures

The convergence of urban expansion, deteriorating infrastructure, and a changing climate will escalate the risks of stormwater pollution and urban flooding in the coming decades. Using outputs from an ensemble of global climate models to drive a high spatial resolution stormwater model, we analyzed climate change impacts on urban stormwater runoff and control measures for 23 cities across the United States. Runoff model outputs for two future emissions scenarios ending in 2055 were compared against a historical scenario to assess changes. All cities showed increases in average annual stormwater runoff, with changes up to 30% over the next 30 years due to a greater frequency of high intensity storm events. Runoff model outputs showed substantial variation across cities with untreated stormwater runoff increasing by as much as 48%. Patterns of future runoff impacts within cities will affect the performance of distributed treatment strategies such as Green Stormwater Infrastructure (GSI) to meet municipal water quality improvement and runoff reduction goals. Results indicate that adoption of adaptable design standards and decision support tools that readily accommodate projected precipitation changes are critical for supporting more resilient designs of stormwater control measures.

Hydrological response of tropical rivers basins to climate change using the GR2M model: the case of the Casamance and Kayanga-Géva rivers basins

The main objective of this research is to evaluate the effects of climate change first on precipitation and temperature, and then on the runoff characteristics of two tropical watersheds located in Senegal and Guinea-Bissau. To achieve this, eighteen General Circulation Models (GCMs) were selected to measure various climate change scenarios under the Shared Socioeconomic Pathways (SSP) SSP1-2.6 and SSP5-8.5, using the reference period of 1985–2014. The GR2M hydrological model was employed to replicate past monthly surface runoff patterns for the Casamance and Kayanga-Géva watersheds. After calibrating and validating the GR2M model, the researchers simulated the predictable effect of climate change on the flow for the near future (2021–2040), medium future (2041–2060), and distant future (2081–2100) for each watershed, using the GCM multi-model ensemble mean. The quantile method was used to correct bias in temperature and precipitation data. The results of bias correction give a correlation coefficient greater than 0.9% for temperatures and 0,6% precipitation between the outputs of the multi-model ensemble and observations used. The results indicate also that all watersheds are expected to experience drier conditions in the near-future, mid-future, and far-future periods under both the SSP1-2.6 and SSP5-8.5 scenarios. Furthermore, the predictable temperature trends consistently show a warmer situation with growing radiative making in the future times. However, the primary factor influencing changes in flow for all watersheds is the projected precipitation changes. The anticipated drier conditions in the near-future, mid-future, and far-future horizons under both scenarios would lead to significantly reduced runoff volumes at the beginning and middle of the rainy season. Consequently, the projected seasonal changes in river flow for all catchments (e.g., under SSP5-8.5 scenario, a decline of -34.47%, -56.01%, and -68.01% was noted, respectively, for the horizons 2050, 2070, and 2090 for the Casamance basin) could lead to new frequent occurrences of drought and water scarcity associated with past hydrological regimes. These scenarios enhance the necessity of improving water management, water prizing, and water recycling policies, to ensure water supply and to reduce tensions among regions and countries.

Assessing Arctic wetting: Performances of CMIP6 models and projections of precipitation changes

The Arctic region is experiencing a notable increase in precipitation, known as Arctic wetting, amidst the backdrop of Arctic warming. This phenomenon has implications for the Arctic hydrological cycle and numerous socio-ecological systems. However, the ability of climate models to accurately simulate changes in Arctic wetting has not been thoroughly assessed. In this study, we analyze total precipitation in the Arctic using station data, multiple reanalyses, and 35 models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6). By employing the moisture budget equation and an evaluation method for model performance with ERA5 reanalysis as a reference, we evaluated the models' capability to reproduce past Arctic wetting patterns. Our findings indicate that most reanalyses and models are able to replicate Arctic wetting. However, the CMIP6 models generally exhibit an overestimation of Arctic wetting during the warm season and an underestimation during the cold season from 1979 to 2014 when compared to the ERA5 reanalysis. Further investigation reveals that the overestimation of wetting during the warm season is largest over the Arctic Ocean's northern part, specifically the Canadian Arctic Archipelago, and is associated with an overestimation of atmospheric moisture transport. Conversely, the models significantly underestimate wetting over the Barents-Kara Sea during the cold season, which can be attributed to an underestimation of evaporation resulting from the models' inadequate representation of sea ice reduction in that region. The models with the best performance in simulating historical Arctic wetting indicate a projected intensification of Arctic wetting, and optimal models significantly reduce uncertainties in future projections compared to the original models, particularly in the cold season and oceanic regions. Our study highlights significant biases in the CMIP6 models' simulation of Arctic precipitation, and improving the model's ability to simulate historical Arctic precipitation could reduce uncertainties in future projections.

Global change impacts on bird biodiversity in South Asia: potential effects of future land-use and climate change on avian species richness in Pakistan.

Evaluating the impact of future changes in land-use and climate on species communities, especially species richness, is one of the most important challenges of current research in ecology and conservation. The impact of environmental changes on species richness depends on its sensitivity (i.e., how strongly a given level of change influences the ecological community) and its exposure (i.e., the amount of change that occurs). To examine the sensitivity, exposure, and potential impact of future environmental conditions on bird communities, we compiled data on bird species richness for Pakistan-a neglected region in macro- or country-scale studies. Since bird species richness strongly varies across seasons due to the seasonal occurrence of migratory species in winter, we compared both wintering (migratory plus resident species) and breeding (resident species only) bird richness. We found breeding and wintering species richness to be sensitive to temperature, precipitation and rainfed cropland by being positively related to these factors. Exposure varied regionally, with projected temperature changes being most profound in northern regions while the strongest projected precipitation changes occurred in central and southern regions. The projected impact of future environmental change were highly heterogeneous across the country and differed between the wintering and breeding communities. Overall, the most negatively impacted region was projected to be the Khyber Pakhtunkha province in the North of Pakistan, due to reductions in precipitation and rainfed cropland, resulting in a projected negative impact, especially on wintering species richness. By highlighting the regional and seasonal bird communities most at risk, our findings provide useful information for policy makers to help devise new policies for mitigating negative impacts of future environmental changes on birds within Pakistan.

Future Drying Subtropical East Asia in Winter: Mechanism and Observational Constraint

Abstract Coupled climate models project robust wintertime wetting trend over midlatitude East Asia under global warming scenarios, but the projected change in precipitation shows large intermodel uncertainty over subtropical East Asia from southern China to southwestern Japan. Based on an ensemble of climate models participating in CMIP6, this study shows that the weakened southern branch westerly jet (SWJ) on the southern side of Tibetan Plateau (TP) plays a key role in suppressing subtropical East Asian precipitation. The SWJ is deflected into southwesterly wind on the southeastern side of TP, bringing ascent and precipitation to subtropical East Asia primarily through isentropic gliding. As a result of the poleward and upward shift of the planetary-scale westerly jet under global warming, the SWJ becomes weaker and it acts to suppress subtropical East Asian precipitation by weakening the southwesterly wind and ascent. The SWJ–precipitation linkage also exists on interannual time scales, but the sensitivity of precipitation to interannual SWJ variability is systematically underestimated by the models compared with observation. The combined effects of the change in SWJ strength and the sensitivity of precipitation to SWJ explain about 40% of the intermodel spread of the projected precipitation changes. Observational constraint on the SWJ–precipitation relationship amplifies the projected drying trend and narrows the intermodel spread. It shows that the regional-averaged precipitation over subtropical East Asia decreases by 3.3% per degree of warming, and the amplitude of precipitation reduction over subtropical East Asia (southern China) is about 1.4 (3.4) times the raw projection.

Temperature and precipitation biases in CORDEX RCM simulations over South America: possible origin and impacts on the regional climate change signal

Precipitation and temperature biases from a set of Regional Climate Models from the CORDEX initiative have been analysed to assess the extent to which the biases may impact the climate change signal. The analysis has been performed for the South American CORDEX domain. A large warm bias was found over central Argentina (CARG) for most models, mainly in the summer season. Results indicate that the possible origin of this bias is an overestimation of the incoming shortwave radiation, in agreement with an underestimation of the relative humidity at 850 hPa, a variable that could be used to diagnose cloudiness. Regarding precipitation, the largest biases were found during summertime over northeast of Brazil (NEB), where most models overestimate the precipitation, leading to wet biases over that region. This bias agrees with models’ underestimation of both the moisture flux convergence and the relative humidity at lower levels of the atmosphere. This outcome suggests that the generation of more clouds in the models may drive the wet bias over NEB. These systematic errors could affect the climate change signal, considering that these biases may not be stationary. For both CARG and NEB regions, models with higher warm biases project higher warming levels, mainly in the summer season. In addition, it was found that these relationships are statistically significant with a confidence level of 95%, pointing out that biases are linearly linked with the climate change signal. For precipitation, the relationship between the biases and the projected precipitation changes is only statistically significant for the NEB region, where models with the largest wet biases present the greatest positive precipitation changes during the warm season. As in the case of biases, the analysis of the temperature and precipitation projections over some regions of South America suggests that clouds could affect them. The results found in this study point out that the analysis of the bias behaviour could help in a better interpretation of the climate change signal.

Investigating Extreme Snowfall Changes in China Based on an Ensemble of High-Resolution Regional Climate Models

Anthropogenically induced global warming intensifies the water cycle around the world. As a critical sector of the water cycle, snow depth and its related extremes greatly impact agriculture, animal husbandry, and food security, yet lack investigation. In this study, five high-resolution climate models are selected to simulate and project snow depth and its extremes over China. The simulation capabilities of models in reproducing the basic climate variables in winter are gauged in terms of spatial and temporal patterns over nine subregions. It is found that the driving global climate model (GCM) can contribute to similar patterns, while the different regional climate model (RCM) schemes lead to large variations in the snowfall accumulating on the land surface. The warming magnitude is larger under a higher representative concentration pathway (RCP) scenario (2.5 °C greater under RCP8.5 than RCP4.5). The distribution of ensemble mean winter precipitation changes is more fragmented because of the relatively low skill in reproducing water-related content in the climate system. The projected precipitation change is larger under RCP8.5 than under RCP4.5 due to the amplification of the hydrological cycle by temperature warming. The projected changes in the ensemble mean snow depth mainly occur over the Tibetan Plateau with a decreasing trend. Only several grids over the Himalayas Mountains and the upper stream of the Yarlung Zangbo River are projected with a slight increase in snow depth. Both the intensity and frequency of extreme snow events are projected to increase in Northeast China and Inner Mongolia, which are important agricultural and animal husbandry production areas in China. The reason behind this projection can be explained by the fact that the hydrological cycle intensified by temperature warming leads to excessive snowfall stacking up during winter. The changes in extreme snowfall events in the future will have a significant impact on China’s agricultural and animal husbandry production and threaten food security.

Present-day warm pool constrains future tropical precipitation

Future changes in tropical precipitation affect the livelihood of the world’s human population and ecosystems. Climate models project an increased rainfall intensification under anthropogenic warming, but uncertainties in the distribution and magnitude of the changes remain large. Here, we identify a strong positive relationship between the present-day Indo-Pacific warm pool size and projected precipitation changes in the central-to-eastern tropical Pacific using multi-model simulations. Models with larger present-day warm pool size project excessive future warming in the eastern tropical Pacific due to intensified ocean stratification which reduces the zonal sea surface temperature gradient of the tropical Pacific, resulting in the weakening of Walker circulation and precipitation increases in the central-to-eastern tropical Pacific. Based on this relationship, uncertainty in the projected precipitation in the central-to-eastern tropical Pacific can be reduced by approximately 25%, which demonstrates that an improved simulation of present-day Indo-Pacific warm pool size is important for reliable tropical precipitation projections.

High resolution projections for extreme temperatures and precipitation over Greece

The present study investigated future temperature and precipitation changes over Greece using the Weather Research and Forecasting (WRF) model. WRF was driven by EC-EARTH over Greece at very high resolution for the historical period (1980–2004), along with projected simulations, in the near future (2025–2049) and far future (2075–2099) under the Representative Concentration Pathways 4.5 (RCP4.5) and 8.5 (RCP8.5). Climatic variables were produced at 5-km grid spacing and 6-h interval. The historical simulation was evaluated against the available station observations. The analysis showed that the model underestimated the maximum temperatures and slightly overestimated the minimum temperatures. Also, the model simulated a small dry bias in precipitation with an excellent representation of the spatial patterns. The model projections for temperature under the two emission scenarios compared to the historical simulation revealed a robust magnitude of future warming with the most pronounced changes predominantly over the eastern areas of the country under the RCP8.5 in the far future. Projected precipitation changes were more evident in the far future with an overall decrease of the annual precipitation all over the eastern part of the country (with islands included) with the most dramatic reductions (above 40%) of seasonal precipitation observed under RCP8.5. Increases in the number of hot days were found everywhere with more pronounced changes over the plain areas under RCP8.5 in the far future. Significant increases of dry days were projected over the eastern part of the mainland and more intensely under RCP8.5 in the far future.

Evaluation of coupled model intercomparison project phase 6 models in simulating precipitation and its possible relationship with sea surface temperature over Myanmar

The study investigated the precipitation variability over Myanmar at the annual and seasonal scales by comparing 12 model outputs from the Coupled Model Intercomparison Project Phase 6 (CMIP6) with gridded observational data provided by the Global Precipitation Climatology Centre (GPCC) from 1970 to 2014. Using Mann–Kendall and Sen’s slope estimator, the trend analysis was assessed. Correlation analysis was also used to investigate the relationship of observational and Ensemble means precipitation with sea surface temperature (SST) anomalies. Results show a better correlation pattern of ENS with observation precipitation than that of individual selected models during the May-October season than that of the annual scale. Meanwhile, UKESM1-0-LL, NESM3, and HadGEM3-CC31-LL show high correlation with a relatively low root-mean-square difference. A few models roughly capture the spatiotemporal patterns of precipitation during MJJASO over Myanmar. The root mean square errors (RMSEs) of MIROC6, CNRM-ESM2-1, CNRM-CM6, and NESM3 are lower than that of ENS, whereas the RMSEs of CESM2, GFDL-CM4, HadGEM3-CC31-LL, GFDL-ESM4, UKESM1-0-LL, MPI-ESM1-2-HR, MRI-ESM2-0, and IPSL-CM6A-LR are higher than that of ENS, for annual precipitation. Heterogeneous correlation coefficients and slope changes are evident within the country at both annual and seasonal periods. Overall, the ENS showed a long-term increasing annual trend. Most of the model exhibited increasing annual trends while some showed decreasing annual trends. The correlation between the annual series and SST anomalies shows stronger correlation coefficient than that of seasonal. Overall, the correlation analysis of the SST anomalies reveals significant positive and negative relationships with the ENS precipitation. We recommend considering future projections of precipitation changes over Myanmar in future work.

Aerosol absorption has an underappreciated role in historical precipitation change

Precipitation change has proven notoriously hard to simulate consistently between global climate models. Aerosol induced shortwave absorption over the historical era is also poorly constrained in both observations and modelling. These factors are closely linked, since absorption induced heating of the atmospheric column inhibits precipitation formation. Here I show that the spread in simulated aerosol absorption in the most recent generation of climate models (CMIP6) can be a dominating cause of uncertainty in simulated precipitation change, globally and regionally. Consequently, until improvements are made in scientific understanding of the key absorbing aerosol types, projections of precipitation change under future anthropogenic emissions will have major, irreducible uncertainties. Black carbon, which has recently been found to have only a weak influence on global surface temperature, regains prominence as a contributor to regional precipitation change and its historical and future evolution.

Observed and projected precipitation changes in the zones of monitoring lakes of Kazakhstan

Observed and projected precipitation changes in the zones of monitoring lakes of Kazakhstan

Projections of precipitation change from CMIP6 based on a new downscaling method in the Poyang Lake basin, China

Study regionThe Poyang Lake (PL) basin, located in the middle and lower reaches of the Yangtze River, China. Study focusThis study investigated the future precipitation variations during 2020–2100 under four shared socioeconomic pathways (SSPs) by downscaling the CMIP6 outputs using a new scale-based downscaling method, which integrated the GWR-KED method and time transfer matrices by linking the precipitation in the historical and future periods. New hydrological insightsCompared with the original CMIP6 outputs, the downscaled precipitation predictions exhibited large improvements, with mean absolute errors (MAEs) improved by a range of 37.49~66.81% and 74.95~75.38% on the seasonal and annual scale, respectively. The downscaled projections showed that the basin would be wetter at the annual scale during the next 80 years. Precipitation in spring would be increase over the entire basin under all future scenarios except SSP1–2.6. Parts of the PL basin exhibit drier trends under the four scenarios in summer, with the largest drying area occurring in the SSP2–4.5 scenario. Except in the SSP2–4.5 scenario, precipitation in autumn has increasing trends in all scenarios across the PL basin. Precipitation in winter shows a drying trend in some areas except in the SSP1–2.6 scenario, with the largest area occurring in SSP5–8.5. The results provide improved precipitation information for policy-making in water resource management and adaptation planning to mitigate Yangtze River floods in the future.

Contrasted Heat Stress in Northern and Southern African Monsoon Biosphere Reserves Explained by Projected Precipitation Changes

Contrasted Heat Stress in Northern and Southern African Monsoon Biosphere Reserves Explained by Projected Precipitation Changes

Response of Tropical Rainfall to Reduced Evapotranspiration Depends on Continental Extent

AbstractFuture projections of precipitation change over tropical land are often enhanced by vegetation responses to CO2 forcing in Earth system models. Projected decreases in rainfall over the Amazon basin and increases over the Maritime Continent are both stronger when plant physiological changes are modeled than if these changes are neglected, but the reasons for this amplification remain unclear. The responses of vegetation to increasing CO2 levels are complex and uncertain, including possible decreases in stomatal conductance and increases in leaf area index due to CO2 fertilization. Our results from an idealized atmospheric general circulation model show that the amplification of rainfall changes occurs even when we use a simplified vegetation parameterization based solely on CO2-driven decreases in stomatal conductance, indicating that this mechanism plays a key role in complex model projections. Based on simulations with rectangular continents we find that reducing terrestrial evaporation to zero with increasing CO2 notably leads to enhanced rainfall over a narrow island. Strong heating and ascent over the island trigger moisture advection from the surrounding ocean. In contrast, over larger continents rainfall depends on continental evaporation. Simulations with two rectangular continents representing South America and Africa reveal that the stronger decrease in rainfall over the Amazon basin seen in Earth system models is due to a combination of local and remote effects, which are fundamentally connected to South America’s size and its location with respect to Africa. The response of tropical rainfall to changes in evapotranspiration is thus connected to size and configuration of the continents.

Added value of high-resolution climate projections over South Korea on the scaling of precipitation with temperature

This study evaluates the resolution dependency of scaling precipitation with temperature from the perspective of the added value of high-resolution (5 km) dynamical downscaling using various kinds of long-term climate change projections over South Korea. Three Coupled Model Intercomparison Project Phase 5 (global climate models (GCMs) with different climate sensitivities, and one pseudo global warming (PGW) experiment, are downscaled by Weather Research and Forecasting one-way double nested modeling system with convective parameterization for the reference (1976–2005) and future (2071–2100) periods under RCP8.5 scenario. A detailed comparison of the driving GCM/PGW, 20 km mother simulation, and 5 km nested simulation demonstrates improved representation of precipitation with increasing resolution not only in the spatial pattern and magnitude for both the mean and the extremes, but also in a more realistic representation of extreme precipitation’s sensitivities to temperature. According to the projected precipitation changes downscaled from both GCM ensemble and PGW, there will be intensified precipitation, particularly for the extremes, over South Korea under the warming, which is primarily contributed by convective precipitation increase that shows higher temperature sensitivity. This study also compares the extreme precipitation-temperature scaling relations within-epoch (apparent scaling) and between-epoch (climate scaling). It confirms that the magnitude and spatial pattern of the two scaling rates can be quite different, and the precipitation change over Korea under global warming is mainly controlled by thermodynamic factors.

Future precipitation in a Mediterranean island and streamflow changes for a small basin using EURO-CORDEX regional climate simulations and the SWAT model

Climate change effect analysis on the hydrologic cycle is essential for the assessment of water management strategies. Climate models can provide projections of precipitation changes in the future, based on greenhouse gas emissions scenarios. In this study the EURO-CORDEX (European COordinated Regional Downscaling Experiment) climate models were first analyzed for the island of Sardinia, against observed precipitation for the historical period of 1976–2005. A Multi-Model Ensemble (ENS) was built, weighting different models based on their performance against observed precipitations. Future projections (2071–2100) were analyzed using the 8.5 RCP emissions scenario to evaluate changes in precipitations. Climate models were then used as climate forcing for the SWAT model (Soil and Water Assessment Tool), aiming to evaluate the effects of such changes on streamflow and runoff of two small catchments located in the South-West Sardinia. A general decrease of mean precipitation values, up to 25 % at yearly scale in South-West Sardinia, is expected for the future, along with an increase of extreme precipitation events. Especially in the eastern and southern areas, extreme events are projected to increase by 30%. Such changes reflect on the hydrologic cycle with a decrease of mean streamflow (ranging from −18% to −25%) and runoff (ranging from −12% to −18%), except in spring, when runoff is projected to increase by 20–30%. This study contributes to the analysis of climate change impacts in the Mediterranean, which is considered a hotspot of the global warming, enhancing the use of modeling tools to investigate possible future scenarios.

Decreases in relative humidity across Australia

How relative humidity is changing is important for our understanding of future changes in precipitation and evaporation. For example, decreases in relative humidity have the potential to increase evaporation and evapotranspiration increasing water scarcity. Since projected precipitation changes are highly uncertain, there is significant research relating precipitation changes to more certain local temperature increases, but such research often assumes relative humidity will remain constant. Here, we investigate how absolute and relative humidity across Australia have changed over 1955–2020. Absolute humidity, measured by dew point temperature, has remained relatively constant, while relative humidity has decreased on average over land by approximately −1%/decade. This suggests that assuming constant relative humidity when predicting future extreme precipitation using temperature or absolute humidity associations may result in over-estimation of future extreme precipitation intensities. As absolute humidity, measured by dew point temperature, was found to be relatively constant, we conclude the decrease in relative humidity is not due to a lack of water available for evaporation but may instead be the result of evaporation not increasing in line with temperature increases.