Climate Impact Studies Research Articles

Indigenous local observations and experiences can give useful indicators of climate change in data-deficient regions

This study demonstrates that Indigenous local observations and experiences can enrich knowledge of climate change, particularly in data-deficient regions that are not adequately covered by weather stations. Paradoxically, these places host groups of Indigenous Peoples who have rich knowledge about their local climates from their many years of constant interactions with the environment. The study used group-based semi-structured interviews to collaborate with keystone elderly participants who had profound knowledge and lived experiences about observed changes in their local environment (n = 13). These participants were identified through theoretical sampling from four remote Indigenous villages of Mbire District in Zimbabwe. The purpose of the study was to identify indicators of climate change impacts from communities believed not to have been much influenced by the scientific construct of climate change. Results revealed that the locals have a keen interest to closely observe changes occurring in their environment, including finer accounts of experiences with climatic events, owing to their predominantly climate-sensitive livelihoods. These results corroborate existing evidence about a warmer and drier climate and the reported increase in the frequency and severity of drought as well as floods in the area, and add finer details to the changes in ecological, hydrological and human systems, which are not sufficiently reported in existing climate impact studies. We also flagged new observations in biological systems as pointers for further intensive investigation. Given the complexity associated with understanding impacts of climate change and the urgent need to refine knowledge about the same, we argued for perforation of the boundaries of climate science to accommodate enriching perceptions of Indigenous communities who have been religiously observing changes happening in their local environment, albeit being relegated.

Convolutional conditional neural processes for local climate downscaling

Abstract. A new model is presented for multisite statistical downscaling of temperature and precipitation using convolutional conditional neural processes (convCNPs). ConvCNPs are a recently developed class of models that allow deep-learning techniques to be applied to off-the-grid spatio-temporal data. In contrast to existing methods that map from low-resolution model output to high-resolution predictions at a discrete set of locations, this model outputs a stochastic process that can be queried at an arbitrary latitude–longitude coordinate. The convCNP model is shown to outperform an ensemble of existing downscaling techniques over Europe for both temperature and precipitation taken from the VALUE intercomparison project. The model also outperforms an approach that uses Gaussian processes to interpolate single-site downscaling models at unseen locations. Importantly, substantial improvement is seen in the representation of extreme precipitation events. These results indicate that the convCNP is a robust downscaling model suitable for generating localised projections for use in climate impact studies.

Investigating major causes of extreme floods using global datasets: A case of Nepal, USA & Thailand

In recent years, the damages caused by flooding has been severe globally, and several research studies indicated extreme precipitations and changes in land-use plays a crucial role. The hydrological and climate impact studies in data-scarce regions are relatively challenging, and several global dataset products have aided in overcoming them. However, their accuracy and reliability vary from climatic regions to the topography of the land surface. Therefore, this study employed global dataset products for precipitation and land use to identify the major flood driver in tropical, subtropical, and temperate regions where severe flooding had occurred in the past decade. The study evaluated the performances of the PERSIANN-CDR, PERSIANN-CCS and PERSIANN precipitation products, and ESACCI-LC land-use product to develop a statistical relationship among the land-use and extreme precipitation variables using the Multiple Linear Regression technique. The result shows that the PERSIANN-CDR estimates were more accurate than others in the selected study basins. The statistical model showed that the combined contribution of both land-use and precipitation to the flood (R2) are 73.9%, 66.7% and 37.4%, for the Mun River Basin (MRB), Thailand, the Bagmati River Basin (BRB), Nepal and the Missouri Little Sioux (MLSB) Basin, USA, respectively. Moreover, it correlated with the flood (R) by 85.9%, 81.7% and 61.1% in the MRB, BRB, and MLSB, respectively. Additionally, the results indicated that the major cause of flooding in MRB and BRB is likely to be the changes in precipitation, while land-use change is likely to be the major cause in the MLSB. The result from the study shall be useful for the researchers, practitioners, and decision-makers in determining the applicability of a suitable precipitation product in data-scarce regions, visualise the major cause of flooding and plan the flood risk management strategies accordingly by minimising the exposure and maximising the resiliency for possible future events.

Does the High Elevation Climate along Mt. Everest can be Represented by Lower Elevation Stations?

Climate change studies of the high mountain areas of the central Himalayan region are mostly represented by the meteorological stations of the lower elevation. Therefore, to validate the climatic linkages, daily observational climate data from five automated weather stations (AWS) at elevations ranging from 2660 m to 5600 m on the southern slope of Mt. Everest were examined. Despite variations in the means and distribution of daily, 5-day, 10-day, and monthly temperature and precipitation between stations located at a higher elevation and their corresponding lower elevation, temperature records in the different elevations are highly correlated. In contrast, the precipitation data shows a comparatively weaker correlation. The slopes of the regression model (0.82–1.13) with (R2>0.74) for higher altitude (5050 m and 5600 m) throughout the year, 0.83–1.12 (R2>0.68) except late monsoon season for the station at 4260 m and 5050 m asl indicated the similar variability of the temperature between those stations. Similarly, Namche (3570 m) temperature changes by 0.81–1.32°C per degree change in corresponding lower elevation Lukla station (2660 m), except for monsoon season. However, inconsistent variation was observed between the station with a large altitudinal difference (2940 m) at Lukla and Kala Patthar (5600 m). In general, climate records from corresponding lower elevation can be used to quantitatively assess climatic information of the high elevation areas on the southern slope of Mt. Everest. However, corrections are necessary when absolute values of climatic factors are considered, especially in snow cover and snow-free areas. This study will be beneficial for understanding the high-altitude climate change and impact studies.

High-resolution climate datasets in hydrological impact studies: Assessing their value in alpine and pre-alpine catchments in southeastern Austria

Study regionCatchments: Wölzerbach, Raab; Styria; Austria. Study focusThe study was carried out to investigate the fitness for purpose of the high-resolution Austrian National Climate datasets (ÖKS15) in hydrological applications. Provided as a standard for climate impact studies in Austria in 2016, the potential of the ÖKS15 data for hydrological impact studies has not previously been assessed. To fill this research gap, we evaluated the ÖKS15 datasets for the recent climate period (1961–2005) to determine their suitability for hydrological analyses. Hydro-meteorological indicators of the ÖKS15 datasets, as well as of the data used to obtain them by bias correcting and downscaling the EURO-CORDEX models, were assessed at different spatio-temporal scales and compared to the raw EURO-CORDEX data. The hydrological model WaSiM was driven with the different datasets to generate the hydrological simulations used for validation against long-term observations. New hydrological insights for the regionHydro-meteorological indicators obtained from ÖKS15 models are comparable to observations and associated with less uncertainty than the raw EURO-CORDEX data. We thus consider the ÖKS15 dataset basically appropriate for hydrological impact studies. However, despite bias correction, some biases in the ÖKS15 data remain and differences in timing, magnitude and spatial characteristics lead to deviations in hydrological indicators at different scales.

Machine-learning-based evidence and attribution mapping of 100,000 climate impact studies

Increasing evidence suggests that climate change impacts are already observed around the world. Global environmental assessments face challenges to appraise the growing literature. Here we use the language model BERT to identify and classify studies on observed climate impacts, producing a comprehensive machine-learning-assisted evidence map. We estimate that 102,160 (64,958–164,274) publications document a broad range of observed impacts. By combining our spatially resolved database with grid-cell-level human-attributable changes in temperature and precipitation, we infer that attributable anthropogenic impacts may be occurring across 80% of the world’s land area, where 85% of the population reside. Our results reveal a substantial ‘attribution gap’ as robust levels of evidence for potentially attributable impacts are twice as prevalent in high-income than in low-income countries. While gaps remain on confidently attributabing climate impacts at the regional and sectoral level, this database illustrates the potential current impact of anthropogenic climate change across the globe.

Rainfall shocks and household welfare: Evidence from northern Ghana

CONTEXTRainfall shocks pose a threat to farmers in rural West Africa especially in the wake of the recurrent climate variability and its impacts on agricultural production. Despite the harm they pose, limited empirical studies exist on the welfare implications of rainfall shocks on farmers' welfare in West Africa. In addition, the potential impacts of rainfall induced commodity and labor market failures have not been given much attention in the empirical literature. OBJECTIVEOur study aimed to analyze the impact of negative rainfall shocks and commodity and labor market failures on farm households' welfare in northern Ghana. Examining the impact of commodity and labor market failures amidst the experience of a negative rainfall shock helps to identify the possible entry points through which the adverse impacts of rainfall deficits may be reduced. METHODSThe study is based on a household survey data from the Africa Rising program, historical daily climate data from the CCAFS-Climate data portal and random rainfall distributions from Monte Carlo simulation. A total of 1168 households were considered in the analysis. We analyze the impact of rainfall shocks and the above-mentioned entitlement failures using a static optimization model that incorporates a crop yield response function. RESULTS AND CONCLUSIONWe found that an increase in the frequency of negative rainfall shocks under a dry future with and without entitlement failures would impact negatively on the total income and consumption levels of both the asset non-poor and asset poor households in the study area. The asset poor households would however bear the brunt of the impact, and the anticipated impact would mostly be yielded through changes in agricultural incomes and expenditure on food purchases. With increasing risk of dry rainfall conditions, total incomes of farmers could decrease by 7.3% to 45.5%. It was found that ignoring potential failures in commodity and labor markets lead to over/underestimation of the impacts of major rainfall deficits on the different types of farmers. The impact of rainfall shocks on the welfare of farmers is scenario and cluster dependent. SIGNIFICANCEThe results have significance for policy formulation and future research. The findings from this study indicate a need for targeting and acknowledgement of the differential impacts of climate shocks on the different farmer groups. Efforts made in future research to incorporate entitlement failures in climate impact studies could produce more informative guide for effective policy formulation.

Understanding the relationship between rainfall and flood probabilities through combined intensity-duration-frequency analysis

The aim of this paper is to explore how rainfall mechanisms and catchment characteristics shape the relationship between rainfall and flood probabilities. We propose a new approach of comparing intensity-duration-frequency statistics of maximum annual rainfall with those of maximum annual streamflow in order to infer the catchment behavior for runoff extremes. We calibrate parsimonious intensity-duration-frequency scaling models to data from 314 rain gauges and 428 stream gauges in Austria, and analyze the spatial patterns of the resulting distributions and model parameters. Results indicate that rainfall extremes tend to be more variable in the dry lowland catchments dominated by convective rainfall than in the mountainous catchments where annual rainfall is higher and rainfall mechanisms are mainly orographic. Flood frequency curves are always steeper than the corresponding rainfall frequency curves with the exception of glaciated catchments. Based on the proposed approach of combined intensity-duration-frequency statistics we analyze elasticities as the percent change of flood discharge for a 1% change in extreme rainfall through comparing rainfall and flood quantiles. In wet catchments, the elasticities tend to unity, i.e. rainfall and flood frequency curves have similar steepness, due to persistently high soil moisture levels. In dry catchments, elasticities are much higher, implying steeper frequency curves of floods than those of rainfall, which is interpreted in terms of more skewed distributions of event runoff coefficients. While regional differences in the elasticities can be attributed to both dominating regional rainfall mechanisms and regional catchment characteristics, our results suggest that catchment characteristics are the dominating controls. With increasing return period, elasticities tend towards unity, which is consistent with various runoff generation concepts. Our findings may be useful for process-based flood frequency extrapolation and climate impact studies, and further studies are encouraged to explore the tail behavior of elasticities.

Representing climate evolution in ensembles of GCM simulations for the Hudson Bay System

Climate impact studies often require a reduction of the ensembles of opportunity from the Coupled Model Intercomparison Project when the simulations are used to drive impact models. An impact model’s nature limits the number of feasible realizations based on complexity and computational requirements or capacities. For the purpose of driving a hydrological model and an ocean model in the BaySys research program, two hierarchical, differently sized simulation ensembles were produced to represent climate evolution for the region of the Hudson Bay Drainage Basin. We compare a 19-member ensemble to a 5-member subset to demonstrate comparability of the driving climate used to produce model results. Ten extreme climate indicators and their changes are compared for the full study region and seven sub regions, on an annual and seasonal basis and for two future climate horizons. Results indicate stronger warming in the North and for cold temperatures and an East-West gradient in precipitation with larger absolute increases to the East and South of the Hudson Bay. Generally, the smaller ensemble is sufficient to adequately reproduce the mean and spread in the indicators found for the larger ensemble. The analysis of extreme climate indicators ensures that the tails of the distribution of temperature and precipitation are addressed. We conclude that joint analysis at the interface of the hydrological and ocean model domains are not limited by the application of differently sized climate simulation ensembles as driving input for the two different modeling exercises of the BaySys project environmental studies, yet acknowledging that impact model output may be dependent on other factors.

Water resources of the Desna river basin under future climate

AbstractClimate change impact on water resources has been observed in Ukraine since the end of the 20th century. For now, only large-scale climate impact studies cover Ukraine's territory, having low credibility for a specific catchment. This study aims to calculate future changes in river discharge, water flow components, and soil water within the Desna basin and evaluate vulnerability trends on this basis. The framework assembles the process-based SWAT (Soil and Water Assessment Tool) model and eight high-resolution regional climate models (RCMs) driven by RCP4.5 and RCP8.5 emission scenarios. The climate models are provided by the Euro-CORDEX initiative and based on three RCMs (RCA4, HIRHAM5, and RACMO22E) forced by five general circulation models (CNRM-CM5, EC-EARTH, IPSL-CM5A-MR, HadGEM2-ES, and MPI-ESM-LR). The results preferably show a moderate increase in the annual discharge until the end of the 21st century. The intra-annual changes of water balance components negatively affect the vegetation period because of higher dryness and temperature stress but reduce flood risk, diffuse pollution, and water erosion in the far future. In the river basin management plan, the highest attention should be paid to adaptive strategies in agriculture because of possible water deficit in the vegetation season under future climate scenarios.

Projected temperature and precipitation changes using the LARS‐WG statistical downscaling model in the Shire River Basin, Malawi

AbstractThis study analyzes local‐scale temperature and precipitation projections in the Shire River Basin (SRB) in Malawi using 10 global circulation models (GCMs) available in the coupled model intercomparison project phase 5 (CMIP5), under two representative concentration pathways (RCP 4.5 and RCP 8.5). For nine stations in the study area, large‐scale maximum temperature (Tmax), minimum temperature (Tmin) and precipitation data from the selected GCMs were downscaled by the sixth version of the Long Ashton Research Station Weather Generator (LARS‐WG6). The mean seasonal and annual change projections for Tmax, Tmin and precipitation during two future periods, that is, the middle future (2041–2070) and late future (2071–2100) periods were analyzed. Modelling results demonstrated that the LARS‐WG model is capable of simulating temperature more accurately than precipitation in the SRB. All 10 GCMs revealed that continually rising temperatures are anticipated in the study area; however, the projected magnitude of change varied across GCMs and between RCPs. Generally, the increase in average Tmax and Tmin was observed to be higher under RCP 8.5 compared with RCP 4.5 due to unmitigated greenhouse gas emissions (GHGs). Future precipitation change results showed more complexity and uncertainty than for temperature; not all GCMs agree on whether there will be positive or negative changes in precipitation and no systematic variations under RCP4.5 and RCP8.5 were observed during the two future time period, illustrating that both GCMs and RCPs are important sources of the relatively large uncertainties in future precipitation projections in the SRB. Thus, this study indicated that uncertainties constrained by both GCMs and RCPs are crucial and need to always be considered when executing climate impact studies and adaptation, particularly at river basin level.

Impact of soil temperature on fruit species within climate change scenarios

Climate change, with its consequent increase in temperatures and precipitation, has significant impacts on soil surface horizons, affecting the establishment, development, and production of crops and food security and safety. Solar radiation influences the thermals of Earth's surface. Apart from radiation, the effects of soil temperature (STe) are specific to fruit species and are mainly modified by humidity, soil cover and air temperature. Increasing the STe within an optimal range promotes the growth of roots and plants and increases the absorption of water and nutrients, photosynthesis, transpiration, efficient use of water and microbial processes. These effects have been demonstrated in several studies on fruit trees and on grape vines, citrus, cape gooseberries, Annonaceae, avocados, olives and prickly pears. However, apart from these positive results, an increased STe as the result of global warming can generate water stress and in turn affect the yield and quality of fruit trees. In terms of effects from cultural practices, mulching with black or blue plastic can increase the soil temperature, and white or silver plastic decreases it. When compared to air temperature, increases in STe in the plants physiology and climate impact studies have been little studied. Therefore, this review aimed to make significant contributions to facilitate decision-making with the goal of reducing the effects of global warming, especially on fruit trees.

Machine Learning Models Applied for Rainfall Prediction

Predicting rainfall is an important step in generating data for climate impact studies. Rainfall predictions are a key process for providing climate impact assessments with inputs. A consistent rainfall pattern is typically good for normal plants; nevertheless, too much or too little rainfall can be disastrous to crops, even deadly. Drought can damage plants and lead to erosion, while heavy rainfall can encourage the growth of destructive fungi. Machine Learning (ML) can be helpful in overcoming such issues; for example, ML can be used to predict rainfall and apply it to foresee crop health and yield. Predictive analysis is a subset of data mining that forecasts future probabilities and patterns. Various sectors like the Agricultural Produce Markets Committee (APMC), Kisaan call centre, etc., can use proposed method, enabling the sector and farmers to obtain information on future precipitation, crop yields and crop health.

Constructing High-Resolution, Bias-Corrected Climate Products: A Comparison of Methods

AbstractHigh-resolution, bias-corrected climate data are necessary for climate impact studies at local scales. Gridded historical data are convenient for bias correction but may contain biases resulting from interpolation. Long-term, quality-controlled station data are generally superior climatological measurements, but because the distribution of climate stations is irregular, station data are challenging to incorporate into downscaling and bias-correction approaches. Here, we compared six novel methods for constructing full-coverage, high-resolution, bias-corrected climate products using daily maximum temperature simulations from a regional climate model (RCM). Only station data were used for bias correction. We quantified performance of the six methods with the root-mean-square-error (RMSE) and Perkins skill score (PSS) and used two ANOVA models to analyze how performance varied among methods. We validated the six methods using two calibration periods of observed data (1980–89 and 1980–2014) and two testing sets of RCM data (1990–2014 and 1980–2014). RMSE for all methods varied throughout the year and was larger in cold months, whereas PSS was more consistent. Quantile-mapping bias-correction techniques substantially improved PSS, while simple linear transfer functions performed best in improving RMSE. For the 1980–89 calibration period, simple quantile-mapping techniques outperformed empirical quantile mapping (EQM) in improving PSS. When calibration and testing time periods were equivalent, EQM resulted in the largest improvements in PSS. No one method performed best in both RMSE and PSS. Our results indicate that simple quantile-mapping techniques are less prone to overfitting than EQM and are suitable for processing future climate model output, whereas EQM is ideal for bias correcting historical climate model output.

Statistical downscaling using principal component regression for climate change impact assessment at the Cauvery river basin

Abstract Climate change impact studies are generally carried out with higher resolution general circulation model (GCM) outputs, which are usually for a global scale, and it is difficult to use the same for a regional scale. GCM simulations require downscaling to get a coarser scale output for local climate impact studies. In this study, an improvised principal component regression (PCR) downscaling technique is adapted to downscale 26 Coupled Model Intercomparison Project Phase 5 (CMIP5) GCM historical outputs. A massive river basin named Cauvery with 35 observation stations is categorized into three subbasins to study the regional climate impacts. In this case, the PCR model performed remarkably well compared to other conventional machine learning models with half the computational time than usual. The test statistics state that the validation of the proposed model illustrates a variance in calibration results of the PCR model, which ranges between 2 and 5%, and a variance in validation, which is less than 7% throughout the study area. Since it is desired to prioritize GCMs to choose the merely suitable models for a strategic climate study, the models were selected based on the PCR model performance. Furthermore, CCSM4, inmcm4, and EC-EARTH model's performance in recreating precipitation statistics over the study area are exceptional.

Climate Variability Indices—A Guided Tour

The objective of this study is to provide a comprehensive review and characterization of selected climate variability indices. While we discuss many major climate variability mechanisms, we focus on four principal modes of climate variability related to the dynamics of Earth’s oceans and their interactions with the atmosphere: the El Niño–Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), Pacific Decadal Oscillation (PDO), and Atlantic Multi-decadal Oscillation (AMO). All these oscillation modes are of broad interest and considerable relevance, also in climate impact studies related to teleconnections, i.e., relationships between climate variations at distant locations. We try to decipher temporal patterns present in time series of different oscillation modes in the ocean–atmosphere system using exploratory analysis of the raw data, their structure, and properties, as well as illustrating the quasi-periodic behavior via wavelet analysis. With this contribution, we hope to help researchers in identifying and selecting data sources and climate variability indices that match their needs.

Evaluation of regional climate models ALARO-0 and REMO2015 at 0.22° resolution over the CORDEX Central Asia domain

Abstract. To allow for climate impact studies on human and natural systems, high-resolution climate information is needed. Over some parts of the world plenty of regional climate simulations have been carried out, while in other regions hardly any high-resolution climate information is available. The CORDEX Central Asia domain is one of these regions, and this article describes the evaluation for two regional climate models (RCMs), REMO and ALARO-0, that were run for the first time at a horizontal resolution of 0.22∘ (25 km) over this region. The output of the ERA-Interim-driven RCMs is compared with different observational datasets over the 1980–2017 period. REMO scores better for temperature, whereas the ALARO-0 model prevails for precipitation. Studying specific subregions provides deeper insight into the strengths and weaknesses of both RCMs over the CAS-CORDEX domain. For example, ALARO-0 has difficulties in simulating the temperature over the northern part of the domain, particularly when snow cover is present, while REMO poorly simulates the annual cycle of precipitation over the Tibetan Plateau. The evaluation of minimum and maximum temperature demonstrates that both models underestimate the daily temperature range. This study aims to evaluate whether REMO and ALARO-0 provide reliable climate information over the CAS-CORDEX domain for impact modeling and environmental assessment applications. Depending on the evaluated season and variable, it is demonstrated that the produced climate data can be used in several subregions, e.g., temperature and precipitation over western Central Asia in autumn. At the same time, a bias adjustment is required for regions where significant biases have been identified.

Identifying major drivers of daily streamflow from large-scale atmospheric circulation with machine learning

Previous studies linking large-scale atmospheric circulation and river flow with traditional machine learning techniques have predominantly explored monthly, seasonal or annual streamflow modelling for applications in direct downscaling or hydrological climate-impact studies. This paper identifies major drivers of daily streamflow from large-scale atmospheric circulation using two reanalysis datasets for six catchments in Norway representing various Köppen-Geiger climate types and flood-generating processes. A nested loop of roughly pruned random forests is used for feature extraction, demonstrating the potential for automated retrieval of physically consistent and interpretable input variables. Random forest (RF), support vector machine (SVM) for regression and multilayer perceptron (MLP) neural networks are compared to multiple-linear regression to assess the role of model complexity in utilizing the identified major drivers to reconstruct streamflow. The machine learning models were trained on 31 years of aggregated atmospheric data with distinct moving windows for each catchment, reflecting catchment-specific forcing-response relationships between the atmosphere and the rivers. The results show that accuracy improves to some extent with model complexity. In all but the smallest, rainfall-driven catchment, the most complex model, MLP, gives a Nash-Sutcliffe Efficiency (NSE) ranging from 0.71 to 0.81 on testing data spanning five years. The poorer performance by all models in the smallest catchment is discussed in relation to catchment characteristics, sub-grid topography and local variability. The intra-model differences are also viewed in relation to the consistency between the automatically retrieved feature selections from the two reanalysis datasets. This study provides a benchmark for future development of deep learning models for direct downscaling from large-scale atmospheric variables to daily streamflow in Norway.

U.S. Interstate Trade Will Mitigate the Negative Impact of Climate Change on Crop Profit

According to the current Intergovernmental Panel of Climate Change report, climate change will increase the probability of occurrence of droughts in some areas. Recent contributions at the international level indicate that trade is expected to act as an efficient tool to mitigate the adverse effect of future climate conditions on agriculture. However, no contribution has focused on the similar capacity of trade within any country yet. The U.S. is an obvious choice given that many climate impact studies focus on its agriculture and around 90% of the U.S. crop trade is domestic. Combining a recent state‐to‐state trade flow dataset with detailed drought records at a fine spatial and temporal resolution, this paper highlights first that trade increases as the destination state experiences more drought and inversely in the origin state. As a result, crop growers' profits depend on both local and trade partners' weather conditions. Projections based on future weather data convert the crop grower's expected loss without trade into expected profit. As such, this paper challenges the estimates of the current climate impact literature and concludes that trade is expected to act as a $14.5 billion mitigation tool in the near future.

Method Uncertainty Is Essential for Reliable Confidence Statements of Precipitation Projections

AbstractPrecipitation events cause disruption around the world and will be altered by climate change. However, different climate modeling approaches can result in different future precipitation projections. The corresponding “method uncertainty” is rarely explicitly calculated in climate impact studies and major reports but can substantially change estimated precipitation changes. A comparison across five commonly used modeling activities shows that, for changes in mean precipitation, less than half of the regions analyzed had significant changes between the present climate and 1.5°C global warming for the majority of modeling activities. This increases to just over half of the regions for changes between present climate and 2°C global warming. There is much higher confidence in changes in maximum 1-day precipitation than in mean precipitation, indicating the robust influence of thermodynamics in the climate change effect on extremes. We also find that none of the modeling activities captures the full range of estimates from the other methods in all regions. Our results serve as an uncertainty map to help interpret which regions require a multimethod approach. Our analysis highlights the risk of overreliance on any single modeling activity and the need for confidence statements in major synthesis reports to reflect this method uncertainty. Considering multiple sources of climate projections should reduce the risks of policymakers being unprepared for impacts of warmer climates relative to using single-method projections to make decisions.