Simulations of daily rainfall for the region of Bologna produced by 13 climate models for the period 1850–2100 are compared with the historical series of daily rainfall observed in Bologna for the period 1850–2014 and analysed to assess meteorological drought changes up to 2100. In particular, we focus on monthly and annual rainfall data, seasonality, and drought events to derive information on the future development of critical events for water resource availability. The results show that historical data analysis under the assumption of stationarity provides more precautionary predictions for long-term meteorological droughts with respect to climate model simulations, thereby outlining that information integration is key to obtaining technical indications.

Droughts are one of the most challenging risks for modern society. Indeed, most countries across the globe are exposed to medium/high drought risk (see Fig.

Drought risk index score by countries. It combines information on droughts hazard, exposure, and vulnerability. Higher values indicate a higher risk of drought. Source: WRI Aqueduct (

Multiyear droughts are rare extreme events, which are related to large-scale atmospheric teleconnections whose dynamics are dictated by chaotic behaviours. An implication of the rare occurrence of multiyear droughts is the limited availability of data to decipher their frequency and train prediction models. For the same reason, it is also difficult to predict how climate change may exacerbate drought risk. Indeed, the warmer climate is changing the hydrological cycle and further affects precipitation

The above reasoning highlights the key role of predictions in mitigating the risk of multiyear droughts and designing adaptation procedures. Future climatic scenarios are usually generated by general circulation models (GCMs), which attempt to simulate future climate variables under given scenarios of CO

The reliability of rainfall simulated by GCMs has been analysed by several studies that focused primarily on the generation of climate models that preceded CMIP6

The present study aims at inspecting the ability of selected CMIP6 models in simulating regional-scale climate by focusing on meteorological drought occurrence. We first compare simulated statistics with those of one of the longest daily rainfall records globally available: the Bologna rainfall series, whose observation period dates back to 1813. We decided to adopt an observed record as a baseline instead of a reanalysis to take advantage of an extraordinarily long observation period. Second, we assess meteorological drought changes up to 2100.

The purpose of our study is twofold: (i) to evaluate the ability of up-to-date GCMs in simulating the statistics of observed precipitation by focusing on multiyear meteorological droughts and (ii) to infer how precipitation and drought risk will change in the future. The paper is organized as follows: Sect.

Italy is one of the first countries that started to systematically collect meteorological observations. Meteorological instruments and a network of observations were developed by Galileo’s scholars and operated in the 17th century already. The rainfall series collected in Padua since 1725 is the longest daily record in the world, and five other rainfall stations have been continuously in operation – with few missing values – since the 18th century (Bologna, Milan, Rome, Palermo, and Turin). Therefore, a data set of enormous value has been accumulated in Italy over the last 3 centuries

Daily rainfall records in Bologna during 1813–2021, with 10-year moving average (red line).

Rainfall observation in Bologna at a daily timescale dates back to 1714. The series in continuous from 1813 onwards.

The daily rainfall series observed in Bologna from 1813 to 2021 can be obtained from the European Climate Assessment & Dataset (ECA & D) (

Figure

GCM simulations for both historical (1850–2014) and future (2015–2100) periods are publicly available from the Copernicus database (

From each model, an ensemble simulation is generated for different initial conditions, initialization methods, physics versions, and forcing datasets. Similarly to

Table

Description of 13 GCMs from CMIP6 used in this study.

Simulations by GCM are provided at the grid scale. To compare them with observed data, one should take into account the potential bias that may be introduced by subgrid variability. For the considered timescale subgrid variability is expected to be limited in the region of Bologna. In fact, we focus on monthly and annual rainfall data, which exhibit low spatial variability in the region (see the annual reports of the Regional Agency for Environmental Protection at

To compensate for potential bias, we applied bilinear spatial interpolation to estimate the model prediction for Bologna depending on the four nearest GCM grid points. Moreover, we applied quantile delta mapping (QDM) to correct bias with respect to the observed daily rainfall series.

QDM

First, we compute the empirical frequency of non-exceedance

The reliability of GCM simulations with and without bias correction is evaluated by focusing on different temporal scales to obtain a comprehensive picture of model performances.

To assess the performance of each of the considered CMIP6 GCMs in reproducing the statistics of monthly data during the historical period (1850–2014), we use the “combined probability–probability” (CPP) plot

The CPP plot compares the probability distributions

To make the CPP plot, first a realization

The goodness of the simulation of monthly and seasonal rainfall averaged over the observation period is evaluated by a graphical comparison with the observed values and the Taylor diagram

To test the GCM's reliability in simulating multiyear meteorological droughts we apply run theory

In detail, the long-term mean rainfall

Identification of multiyear drought events and characteristics through run theory.

Once a multiyear drought has been identified, drought duration is the time span between the start and the end of the event, and drought severity is computed as the cumulative rainfall deficit with respect to

Statistics of future projections of annual and seasonal rainfall of the 13 considered GCMs under the three considered scenarios are compared with observed and simulated statistics of the historical period to evaluate future climate change in the Bologna region. For a detailed comparison of seasonal rainfall, the future time horizon is divided into near-future (2030–2059) and far-future (2070–2099) time windows. The multi-model median and the 25th–75th percentiles of the projections given by the GCMs are considered for each scenario to represent the associated ensemble uncertainty.

Combined probability–probability plot of observed and GCM-simulated monthly rainfall in five time windows during the historical period before bias correction.

Figures

Combined probability–probability plot of observed and GCM-simulated monthly rainfall in five time windows during the historical period after bias correction.

While each individual model displays consistent performance in terms of probability distribution across various time windows with only minor differences, the results do not allow easy identification of the optimal model for a given time window. Before bias correction, MPI-ESM1-2-LR generally underestimates monthly rainfall for all periods while some other models (ACCESS-CM2, GFDL-ESM4, and MIROC6) end up with a prevailing overestimation. CMCC-ESM2 and CanESM5 relatively well capture the low rainfall, while underestimating high rainfall. The remaining models (e.g. FGOALS-g3, IPSL-CM6A-LR, and INM-CM4-8) generally fit the observed distribution well in some time windows while exhibiting slight departures in other periods. The multi-model ensemble satisfactorily simulates the mean value while overestimating and underestimating the low and high rainfall, respectively.

As expected, after bias correction all models show a better performance in reproducing probability distributions in different periods except FGOALS-g3 and INM-CM4-8, which slightly underestimate low rainfall after QDM. The performance of the ensemble remains somewhat consistent, although for some periods one notes an improvement in the fit of the mean value. In general, it is confirmed that bias correction improves the model performances for historical simulations. The obvious limit of QDM is the requirement of an extended data set of historical data.

For the whole historical period, Fig.

Comparison of sample probability density of annual rainfall for observations and GCM historical simulations.

Table

Lag-1 autocorrelation coefficient between observed annual rainfall and each historical simulation before and after bias correction.

OBS is observation data, MME mean is multi-model ensemble mean, and numbers indicate models: 1 – ACCESS-CM2; 2 – CMCC-ESM2; 3 – CanESM5; 4 – EC-Earth3-Veg-LR; 5 – FGOALS-g3; 6 – GFDL-ESM4; 7 – INM-CM4-8; 8 – INM-CM5-0; 9 – IPSL-CM6A-LR; 10 – MIROC6; 11 – MPI-ESM1-2-LR; 12 – MRI-ESM2-0; 13 – NorESM2-MM.

Figure

Annual cycle of historical (1850–2014) mean monthly rainfall (mm per month) of the MME mean and CMIP6 models against observation data.

Figure

Annual cycle of historical (1850–2014) mean monthly rainfall (mm per month) of the MME mean and CMIP6 models against observation data after bias correction.

Taylor diagram of

Figure

Figure

Taylor diagram of

Tables

Mean values over the considered period of drought frequency (DF), duration (DD), intensity (DI), and maximum deficit (MD) for multiyear meteorological droughts exhibited by observed data (1850–2014) and reproduced by models before bias correction for the historical (1850–2014) and future (2015–2100) periods under the two considered scenarios.

The unit of drought frequency is times per year and the unit of drought duration is years. OBS and HIS are observed data and historical simulation. MME mean is multi-model ensemble mean, and different numbers indicate different models: 1 – ACCESS-CM2; 2 – CMCC-ESM2; 3 – CanESM5; 4 – EC-Earth3-Veg-LR; 5 – FGOALS-g3; 6 – GFDL-ESM4; 7 – INM-CM4-8; 8 – INM-CM5-0; 9 – IPSL-CM6A-LR; 10 – MIROC6; 11 – MPI-ESM1-2-LR; 12 – MRI-ESM2-0; 13 – NorESM2-MM.

Mean values over the considered period of drought frequency (DF), duration (DD), intensity (DI), and maximum deficit (MD) for multiyear meteorological droughts exhibited by observed data (1850–2014) and reproduced by models after bias correction for the historical (1850–2014) and future (2015–2100) periods under the two considered scenarios.

The unit of drought frequency is times per year and the unit of drought duration is years. OBS and HIS are observed data and historical simulation. MME mean is multi-model ensemble mean, and different numbers indicate different models: 1 – ACCESS-CM2; 2 – CMCC-ESM2; 3 – CanESM5; 4 – EC-Earth3-Veg-LR; 5 – FGOALS-g3; 6 – GFDL-ESM4; 7 – INM-CM4-8; 8 – INM-CM5-0; 9 – IPSL-CM6A-LR; 10 – MIROC6; 11 – MPI-ESM1-2-LR; 12 – MRI-ESM2-0; 13 – NorESM2-MM.

The results highlight that FGOALS-g3, INM-CM5-0, MIROC6, MPI-ESM2-0, and NorESM2-MM simulate drought frequency (DF) fairly well. Notably, all models fail to replicate the drought duration (DD), drought intensity (DI), and maximum deficit (MD). For instance, IPSL-CM6A-LR and INM-CM4-8 show the best performance in simulating DD, which, however, is underestimated by about 10 % by the best simulation. Although MPI-ESM1-2-LR presents the highest value of DI and MD against all the models, marked underestimation with respect to the observations still occurs. The MME mean displays relatively good performance in terms of DF but still underestimates DD, DI, and MD. In detail, the MME mean DD is underestimated by about 17 %, while DI and MD for observations are even nearly 34 % and 39 % higher than the MME mean, respectively.

After QDM, a slight improvement is obtained for the simulation of DF. The MME mean confirms its satisfactory performance, although six models still end up with underestimation. Notably, all models still fail to replicate the DD, DI, and MD, with the only exception being INM-CM4-8, which satisfactorily reproduces drought characteristics. In general, the impact of QDM varies depending on each model and drought behaviour.

QDM slightly mitigates the underestimation by the MME mean of DD, DI, and MD, which, however, remain 12 %, 10 %, and 12 % lower than observations, respectively.

Figure

Time series of annual rainfall for both the historical simulation and future projections after bias correction (mm yr

To inspect the temporal progress of changes, the annual cycle of rainfall after bias correction is considered and the future period is divided into the near future (2030–2059) and far future (2070–2099) related to the present-day simulation (1985–2014). Figure

Monthly mean rainfall (mm per month) for multiple models in the present day (1985–2014), near future (2030–2059), and far future (2070–2099) under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios. Thick lines indicate multi-model medians, while shading indicates the 25th–75th percentile model ranges.

Table

The future changes in DF with respect to historical simulations are not remarkable. DD, DF, and MD are generally underestimated with respect to historical data. In fact, the values of DD, DI, and MD for the MME mean under SSP2.6 and SSP8.5 are both lower with respect to observations. Moreover, DD of all models except GFDL-ESM4 is shorter under SSP2.6 with respect to observed data. Only the MME mean and models CMCC-ESM2, MIROC6, and MPI-ESM1-2 show a consistent increase in DD when turning from the historical simulation to SSP2.6 and SSP8.5. Nearly all models show an increase in DI with respect to historical simulations for at least one future scenario, and all models except FGOALS-g3 and MIROC6 show a more intense drought under SSP8.5 than SSP2.6. Changes in MD are similar to DI. The above considerations show that historical data depict a worse future in terms of multiyear droughts with respect to simulations before QDM.

Table

The present study refers to the region of Bologna, where the availability of a 209-year-long daily rainfall series allows us to make a unique assessment of GCMs' reliability and their predicted changes in rainfall and drought risk. The results show that GCMs provide a satisfactory simulation of rainfall seasonality, while statistics of rainfall series estimated for the long-term historical period exhibit discrepancies among models and limited reliability in some cases. In particular, the GCMs show weakness in capturing the correlation of annual rainfall, thereby implying a possible lack of fit in the simulation of cycles.

In fact, our focus is concentrated on the statistics of multiyear droughts. We found that the multi-model ensemble can satisfactorily simulate the mean frequency of droughts during the historical period. Conversely, the mean duration, intensity, and maximum deficit of multiyear droughts are underestimated.

Bias correction with QDM improves the simulation of the statistics of the monthly and annual series, while it does not show consistent enhancements in capturing the correlation of annual rainfall and the distribution of seasonal rainfall. The improvement by QDM to simulate drought characteristics is limited. Indeed, future projections by the multi-model ensemble of multiyear droughts depict a similar risk to historical observations, even after bias correction and adopting the most critical emission scenario.

Our results suggest that validation at the local scale of GCM simulations is an essential step to inform downscaling procedures and correction techniques to make sure that model predictions are consistent with the local features of climate. However, extreme events like multiyear droughts are infrequent, and therefore validating their predicted statistics is particularly challenging.

Therefore, the identification of future drought risk, which one would expect to be increased under climate change, remains a challenge, especially if we consider that the reliability of bias correction depends on the availability of observed historical data. For some situations, classical engineering methods for critical event estimation under the assumption of stationarity, with appropriate integration of the information provided by climate models to account for climate change, may still be the most precautionary approach. Therefore, rigorous use and comprehensive interpretation of the available information are needed to avoid mismanagement by also taking into account that the impact of multiyear meteorological droughts is likely to be exacerbated by further pressure on water resources due to increasing population and water demand.

The historical rainfall series observed in Bologna can be obtained from

AM proposed the main research question and supervised the work. RG made the computational analysis, elaborated additional research ideas, and prepared the paper.

The contact author has declared that neither of the authors has any competing interests.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

We thank Zhiqi Yang and Zhanwei Liu for the helpful discussions. We also thank four anonymous reviewers and the editor for their insightful reviews.

Rui Guo was supported by the China Scholarship Council (CSC) Scholarship, no. 202106060061. Alberto Montanari received partial funding from the RETURN Extended Partnership, financed by the National Recovery and Resilience Plan – NRRP, Mission 4, Component 2, Investment 1.3 – D.D. 1243 2/8/2022, PE0000005.

This paper was edited by Lelys Bravo de Guenni and reviewed by four anonymous referees.