MainDespite progress towards global eradication, malaria remains the single deadliest climate-sensitive infectious disease. Malaria transmission is highly responsive to temperature, driven by both the life cycle of the ectothermic mosquito vectors (Anopheles spp.) and the thermal sensitivity of the parasites (Plasmodium spp.) themselves3,4. In laboratory conditions, Plasmodium falciparum transmission by Anopheles gambiae peaks around 25 °C and becomes negligible below approximately 16 °C or above approximately 34 °C (refs. 3,4,5). Given these biological constraints, climate change has become a major concern for populations potentially at risk of malaria in southern and high-elevation East Africa, where temperatures may no longer be prohibitive to malaria transmission6,7. Conversely, in West and central Africa—where the burden of malaria is highest—many studies have suggested that climate change will reduce or eventually preclude transmission6,8,9.These risks were among the first proposed health impacts of climate change10,11, but have been surprisingly contentious, and even described as ‘hot air’12 and ‘dangerous pseudoscience’13. At the turn of the century, many malaria experts argued that observed warming trends were incompatible with long-term reductions in malaria prevalence across Africa, and warned that other factors such as drug resistance and funding instability posed a more serious threat to malaria eradication12,14,15. Malaria resurgence in the East African highlands became a particular point of contention, with over a dozen studies arguing for16,17,18,19,20 or against21,22,23,24,25,26,27 climate change as a substantial driver. Today, malaria experts generally agree that climate change has contributed to elevational shifts in malaria epidemics28,29 and the geographical ranges of mosquito vectors30. However, the cumulative effect of climate change on the burden of malaria is still an open question: recently, Snow et al.2 examined the past century of continent-wide changes in malaria prevalence and concluded that observed trends could not be neatly explained by climate change, but did so based only on visual correspondence between moving averages of rainfall, minimum temperature and malaria prevalence over the entire continent.In this study, we revisited these debates by applying state-of-the-art methods from detection and attribution, an area of climate science that quantifies the historical and real-time climate impacts of anthropogenic greenhouse gas emissions31. These methods underpin the scientific consensus on human-caused climate change, and are regularly used to identify the role of climate change in the intensity, frequency and distribution of specific extreme events (for example, heatwaves, heavy precipitation and droughts)32,33,34. However, attribution remains challenging for the downstream effects of anthropogenic climate change on people and ecosystems, and methodological frameworks for impact attribution are still comparatively underdeveloped35,36. Applications to infectious disease dynamics are especially challenging, as relationships between climate and disease transmission are often complex, nonlinear and confounded by human intervention, and few epidemiological datasets exist with sufficient spatial and temporal scope to resolve these relationships. As a result, hundreds of studies have tested for correlations between climate and observed changes in disease incidence or prevalence, but very few have shown that these changes are causally attributable to anthropogenic climate change1.Here we drew on frameworks from climate science, econometrics and epidemiology to conduct an end-to-end impact attribution study (per ref. 36), measuring the direct effect of anthropogenic climate change on long-term trends in the burden of an infectious disease. We applied this framework to estimates of P. falciparum malaria prevalence in children 2–10 years of age in sub-Saharan Africa (PfPR2−10), which experiences roughly 95% of the global burden of malaria (with 80% of deaths in children under 5 years of age)37. We analysed a recently published dataset with unparalleled resolution and scope (Fig. 1), consisting of 50,425 surveys spanning more than a century (1900–2016)2, which we aggregate to 9,875 monthly average values at the first administrative (state or province) level. These data capture a snapshot of population-wide prevalence at a moment in time (that is, cases of active malaria infection per child; versus, for example, incidence rate: new cases per child per year). Leveraging climate econometric methods38,39,40, we developed a panel regression model that isolates the role of temperature and extreme precipitation from other confounding factors that also shape malaria endemicity (Fig. 2; see Methods for details). Nonparametric controls in the model (that is, fixed effects) account for regional differences in seasonality, time periods with concerted elimination efforts and other spatiotemporal variation not explained by identifiable factors, such as socioeconomic or ecological differences between populations. We applied this econometric model to make predictions based on ten sets of paired historical climate simulations with and without anthropogenic climate forcing, and estimate the effect of anthropogenic climate change on malaria prevalence from 1901 to 2014, accounting for both statistical and climatological uncertainty (Fig. 3). Finally, we projected how future climate change could further alter malaria prevalence between 2015 and 2100, based on three future climate change scenarios for low (shared socioeconomic pathway 1–representative concentration pathway 2.6 (SSP1–RCP2.6)), intermediate (SSP2–RCP4.5) and high (SSP5–RCP8.5) future greenhouse gas concentrations (Fig. 4).Fig. 1: Malaria prevalence observations from 1900 to 2016.a, The total number of malaria prevalence surveys in children 2–10 years of age in the twentieth and early twenty-first century, as measured by Snow et al.2 and aggregated to the first administrative unit. b, Mean reported prevalence of childhood malaria over the entire sample (1900–2016, with temporal coverage varying across space). c, Observed trends in malaria prevalence, broken down by Global Burden of Disease study regions (see main text): each point is a single survey in the original dataset (and the sample size, n, is the total number of surveys) and generalized additive models were used to construct estimated trend lines (shown in solid blue, with the grey shading showing the 95% CI of the model). The pink vertical bars indicate notable periods of successful malaria prevention intervention: the Global Malaria Eradication Programme (1955–1969) and the modern period, including the Roll Back Malaria programs and Global Technical Strategy (2000–2015).Fig. 2: Empirical estimates of prevalence–climate relationships and predictions of climate change impacts from 1901 to 2100.a, The estimated relationship between temperature and prevalence (point estimate is the solid black line; 90% CI is the dashed black line; and 1,000 random draws from the variance–covariance estimate are in red). The histogram shows the distribution of monthly temperatures across the estimation sample, with regional median temperatures shown as vertical lines. b,c, The effects of extreme precipitation events (flood (b) and drought (c)) on prevalence in the month the events occur (0 lag) and after time has passed (1-month, 2-month and 3-month lags), as well as the cumulative impact across the first 4 months. d, The effects on prevalence of two key intervention periods (1 refers to 1955–1969; and 2 refers to 2000–2015) during which large-scale malaria prevention programmes were implemented across the sub-continent. In panels b–d, point estimates (black) are accompanied by resampled draws shown as boxplots, where the box spans the 25–75th percentiles, the centre line indicates the median, and the whiskers extend to the 5th and 95th percentiles (blue, brown and pink). Grey horizontal lines show the zero isocline (no estimated effect on prevalence). e, Predicted change in prevalence attributable to anthropogenic climate change in the recent past (real historical climate is given in blue; counterfactual without anthropogenic warming is in grey) and in the future for low (SSP1–RCP2.6 in dark blue), intermediate (SSP2–RCP4.5 in pink) and high (SSP5–RCP8.5 in green) emission scenarios. The thick lines are the mean estimates accounting for both statistical and climate uncertainty; the shading indicates the 5th and 95th percentiles and is truncated at the lower axis limits for visualization purposes only (the full interval is shown in Extended Data Fig. 9). Historical estimates are shown relative to an average baseline across 1901–1930. Future estimates are shown relative to a baseline across 2015–2020, added to the end-of-historical baseline (2010–2014). The dashed grey line indicates 2014, the end of the historical baseline. Years with incomplete predictions due to lag effects (1901 and 2015) are not displayed.Fig. 3: Historical changes in malaria prevalence attributable to anthropogenic climate change from 1901 to 2014.a, Estimated change in prevalence attributable to anthropogenic climate change in each administrative unit, based on the difference between the historical climate in 2010–2014 and a counterfactual scenario for the same period simulated without anthropogenic warming. Sign uncertainty is the percentage of the 10,000 simulations that estimate an increase (for positive point estimates) or decrease (for negative point estimates) in prevalence due to anthropogenic climate change. An uncertainty of 0% implies that all simulations predict a positive or negative trend, whereas an uncertainty close to 100% indicates a near-even split of simulations showing an increase or decrease in prevalence. b, Estimated change in prevalence attributable to anthropogenic climate change (difference between factual and counterfactual scenario in 2010–2014) in each administrative polygon, compared with the baseline mean temperature at the start of the twentieth century (averaged over 1901–1930); the error bars indicate both 90% (thicker lines) and 95% (thinner lines) CIs. Points and lines are coloured based on the 90% CI: blue for negative effects, red for positive effects and grey for non-significant effects. c, Estimated change in prevalence attributable to anthropogenic climate change in 2010–2014 in each administrative polygon, compared with average elevation; the error bars indicate both 90% and 95% CIs, coloured as in panel b. d, Predicted historical changes in prevalence by year, broken down by region. As in Fig. 2, predictions based on true historical climate (blue) are compared with counterfactual predictions without anthropogenic warming (grey), relative to a 1901–1930 baseline. The thick lines are the mean estimate across all 10,000 simulations; for visualization purposes, the shading indicates the 90% CI and is truncated at the upper and lower axis limits. Plots begin in 1902 with the first full year of predictions (due to lag effects). Black dashed lines in b–d show the zero isocline (no change in prevalence).Fig. 4: Projected future changes in malaria prevalence driven by climate change from 2015 to 2100.a, Projected climate-driven changes in prevalence by the end of the century (2096–2100), compared with the present day (2015–2020), for an intermediate emissions scenario (SSP2–RCP4.5). Sign uncertainty is the percentage of the 10,000 simulations that estimate an increase (for positive point estimates) or decrease (for negative point estimates) in prevalence due to future climate change. An uncertainty of 0% implies that all simulations predict a positive or negative trend, whereas an uncertainty close to 100% indicates a near-even split of simulations showing an increase or decrease in prevalence. b, Projected change in prevalence due to climate change by the end of the century (2096–2100) in each administrative polygon, estimated for SSP2–RCP4.5, compared with the baseline mean temperature at the start of the twentieth century; the error bars indicate both 90% (thicker lines) and 95% (thinner lines) CIs. The points and lines are coloured based on the 90% CI: blue for negative effects, red for positive effects and grey for non-significant effects. c, Projected change in prevalence due to climate change by the end of the century (2096–2100) in each administrative polygon, estimated for SSP2–RCP4.5, compared with average elevation; the error bars indicate both 90% and 95% CIs, coloured as in panel b. d, Projected changes in prevalence by year across all scenarios by region. Projections are given relative to the mean from 2015 to 2020, and as in Fig. 2, and the line colour indicates scenario (SSP1–RCP2.6 in blue, SSP2–RCP4.5 in pink and SSP5–RCP8.5 in green). The thick lines are the mean estimate across all 10,000 simulations; for visualization purposes, the shading indicates the 90% CI, and is truncated at the upper and lower axis limits. Plots begin in 2016 with the first full year of predictions (due to lag effects). Black dashed lines in b–d show the zero isocline (no change in prevalence).A robust signal of climate sensitivityOver the past century, the prevalence of childhood malaria has exhibited a strong concave relationship with temperature (Fig. 2a). Closely aligning with theoretical expectations that P. falciparum transmission by A. gambiae mosquitoes should peak around 25.6 °C (ref. 3), observed values of PfPR2−10 in our dataset peak around a monthly mean temperature of 25.8 °C (Extended Data Fig. 1). On the basis of these biological expectations, we estimated the effect of temperature as a quadratic response in a panel regression model, and found that prevalence peaks at 24.9 °C (95% CI 21.0–27.1 °C). These results confirm that laboratory-based studies approximate malaria epidemiology in real populations quite well, and that temperature has a substantial role in transmission dynamics: a 10 °C increase or decrease from the optimal temperature lowers prevalence by approximately 8 percentage points (p.p.).The relationship between precipitation and malaria prevalence is more complex, and probably less consequential for historical trends (Fig. 2b,c). Contemporaneous monthly precipitation exhibits a nonlinear, but highly uncertain, relationship to prevalence (Supplementary Fig. 1). To parsimoniously capture nonlinear effects and disentangle divergent impacts of low and high precipitation, we defined precipitation shocks with two binary indicator variables, equal to one when monthly precipitation falls below the 10th percentile (we labelled this a ‘drought shock’) or above the 90th percentile (‘flood shock’) of monthly precipitation calculated for each subnational unit. Although drought and flood events are complex phenomena, which develop from the combination of multiple factors (for example, soil conditions and topography) in addition to rainfall over varying timescales, we used this drought–flood terminology as shorthand to indicate extremely low or high precipitation months. Most effects are statistically insignificant, but we found that drought shocks tend to decrease malaria prevalence 1–2 months later, whereas conversely, flood shocks have a positive effect on prevalence 2–3 months later. These effects and their timing are broadly consistent with expectations about how precipitation mediates the availability of a mosquito breeding habitat: dry out kills larvae and eggs41, whereas inundation creates a new breeding habitat42. Sensitivity analyses were also weakly suggestive of another established mechanism43, in which floods may wash away eggs and larvae, reducing transmission in the shorter term (Supplementary Figs. 1 and 2). Overall, extreme precipitation has a measurable effect on malaria prevalence, but may be less important than temperature; however, given the sparsity of weather station data44 and the uncertainty of precipitation reconstructions45, it is also possible that our analysis unavoidably underestimates the effect of precipitation due to measurement error.Additional sensitivity analyses reinforce that these prevalence–climate relationships are both statistically robust and biologically consistent. Key findings are stable through time (Supplementary Fig. 3) and are generally insensitive to alternative model specifications, such as the inclusion of lagged effects of temperature (Extended Data Fig. 2); higher-order polynomial effects of temperature (Supplementary Fig. 4); alternative definitions of drought and flood shocks (Supplementary Figs. 2, 5 and 6); alternative climate data (Extended Data Fig. 3 and Supplementary Table 1); controlling for the diagnostic test type (Supplementary Table 2); alternative methods of capturing uncertainty (Supplementary Table 3 and Supplementary Fig. 7); and alternative spatiotemporal controls, which account differently for variation over space (at region, country and state levels), time (including yearly and monthly variation) and interactions among space and time (Extended Data Table 1 and Extended Data Fig. 4). Results are robust to dropping individual countries, years or months from the sample (Supplementary Fig. 8) and to estimating a grid-level regression that does not aggregate prevalence or weather across space (Supplementary Fig. 9). Temperature and flood effects are strongest in rural areas (Extended Data Fig. 5), consistent with negative direct relationships between urbanization and malaria prevalence identified in previous work46,47, as well as specific risks associated with proximity to natural water bodies or rain-fed cropland in rural areas48. However, drought effects are highly uncertain, particularly in urban areas. Finally, our statistical model recovers large reductions in mean prevalence during two key intervention periods (1955–1969 and 2000–2015) that saw substantial malaria prevention programmes across the continent (Fig. 2d).Overall, we found a robust relationship between malaria prevalence and climate, including both temperature and rainfall, that is consistent with expectations based on experimental and ecological evidence. Although climate change is unlikely to be the strongest driver of global trends in malaria endemicity (see the next section), at a local scale, the month-to-month and year-to-year impacts of climate variability can be comparable in scale to the impacts of major interventions (Fig. 2d). These findings suggest that malaria programmes should be responsive to climate at local and national scales, and weather-based early-warning systems could be useful to anticipate near-term disease dynamics49.Historical impacts of climate changeWe found that anthropogenic climate change has, more likely than not, been responsible for a small increase in the average prevalence of childhood malaria across sub-Saharan Africa since 1901 (Fig. 2e). Compared with counterfactual simulations without anthropogenic climate forcing, we estimated that by 2010–2014, anthropogenic climate change had caused an increase in continental mean PfPR2−10 of 0.07 p.p. (95% CI −0.41 to 0.60 p.p.). Simulations with an attributable increase in continent-wide mean prevalence outnumber those with losses by a factor of 1.4 (proportion P+ of 10,000 paired factual versus counterfactual simulations with a positive difference in prevalence = 0.59). These increases are almost entirely driven by rising temperatures from anthropogenic climate forcing; the effects of drought and flood events on prevalence show no distinguishable signal from anthropogenic climate forcing over time (Extended Data Fig. 6).This overall trend masks substantial regional heterogeneity in historical climate change impacts (Fig. 3a and Extended Data Fig. 7), driven almost entirely by elevational and latitudinal gradients in temperature (Fig. 3b,c). For example, attributable changes in prevalence across southern Africa are high in both magnitude and certainty, with an overall increase of 0.60 p.p. (95% CI −0.24 to 1.61 p.p.; P+ = 0.91), nearly an order of magnitude greater than the continental mean (Fig. 3d). By contrast, climate change has contributed to much lower malaria prevalence in West Africa (mean = −0.40 p.p.; 95% CI −0.96 to 0.04 p.p.; P+ = 0.04), where temperatures already often exceed the biological optimum for transmission. In the central African basin, a stronghold of malaria endemicity with average temperatures close to the 25 °C optimum, the change in prevalence attributable to anthropogenic climate change is positive, relatively small and somewhat uncertain (mean = 0.17 p.p.; 95% CI −0.30 to 0.70 p.p.; P+ = 0.75). Finally, we estimated a meaningful overall increase in prevalence attributable to anthropogenic climate change in East Africa (mean = 0.32 p.p.; 95% CI −0.30 to 1.00 p.p.; P+ = 0.84), but note that changes in prevalence are distributed unevenly along the steep elevational gradient: increases of up to 1–2 p.p. in the Ethiopian highlands and the greater Rift Valley region are accompanied by small but significant local declines throughout lowland areas in Ethiopia, Sudan, South Sudan, Eritrea and Djibouti. On average across high-elevation East Africa (defined as the first administrative units with average elevation more than 1 km), anthropogenic climate change has raised prevalence by 0.81 p.p. (95% CI −0.07 to 1.88; P+ = 0.94).Decomposing these impacts to the monthly level reveals an interplay between space, seasons and shifting burdens (Extended Data Fig. 8 and Supplementary Table 4). In southern Africa, rising temperatures have extended the potential tail end of the malaria season into the winter months (June and July). This supports the long-standing idea that climate change-driven poleward expansion of vector-borne diseases can emerge from shifting season lengths and the constraints they impose on endemicity6,50. In the rest of sub-Saharan Africa, however, climate change impacts generally align with existing seasonality: for example, in lowland central and East Africa, climate change impacts are distributed much more evenly across the year, but have a stronger peak in July and August and a weaker peak in December to February51. Conversely, in West Africa, the negative effects of temperature are concentrated in the hottest months (April and May), at the lowest point in the transmission cycle.Although these effects are meaningful, we caution that they are also far smaller than the reduction achieved through healthcare, mosquito nets, vector control and economic development; previous work with the same dataset has estimated a reduction since 1900 of 16 p.p. (that is, a continent-wide decline in average PfPR2−10 from 40% in 1900–1929 to 24% by 2010–2015 (ref. 2)), whereas our estimates of historical climate change-attributable changes rarely exceed 1.5 p.p. for any individual administrative region. In addition, we estimated that average reductions in prevalence realized during the Global Malaria Eradication Program (1955–1969; estimated reduction averaged over the entire period −4.84 p.p.) and recent programmes such as Roll Back Malaria and the Global Technical Strategy (2000–2015; estimated reduction averaged over the entire period −3.36 p.p.) were substantially larger than the cumulative effects of anthropogenic climate change (Extended Data Table 1). Relatively small and spatially differentiated climate-related changes in burden could have been easily concealed by the greater impact of these programmes, highlighting both the success of elimination programs and the importance of using an empirical approach to isolate the effect of climate from other co-evolving factors.Future impacts of climate changeDespite contemporary trends, we project that within the next quarter-century, anthropogenic climate change will begin to reduce the prevalence of P. falciparum malaria in sub-Saharan Africa (Fig. 2d and Extended Data Table 2). This trend is largely driven by rising temperatures in lowland areas north of the equator, with greater possible reductions in scenarios of higher greenhouse gas emissions (Fig. 4). In these scenarios, temperature-related declines are slightly offset by floods, which will become more frequent across Africa31, although their effect on overall trends is trivial when compared with temperature (Extended Data Fig. 9). Even in a future low-emissions scenario (SSP1–RCP2.6: average global warming across models of +1.8 °C in 2048–2052; +1.9 °C in 2096–2100), increases in prevalence due to historical anthropogenic climate change are projected to essentially be offset by mid-century, stabilizing around an −0.11 p.p. (95% CI −0.48 to 0.20 p.p.) projected decline across sub-Saharan Africa, relative to 2015–2020. In a high-emissions scenario (SSP5–RCP8.5: +2.4 °C in 2048–2052; +5.2 °C in 2096–2100), we project that decreases in prevalence would accelerate over time, reaching an average of −0.29 p.p. (95% CI −0.94 to 0.32 p.p.) by mid-century and −2.04 p.p. (95% CI −5.21 to 0.00 p.p.) by the end of the century, a projected reduction that begins to approach the magnitude of some historical eradication programmes.Although the balance across regions will begin to shift, the geographical pattern of future changes in malaria prevalence is likely to reproduce present-day heterogeneity in impacts, as malaria transmission continues to shift along latitudinal and elevational clines in temperature (Fig. 4 and Extended Data Fig. 10). West Africa is projected to experience the most dramatic transformation, especially in a high-emissions scenario (SSP5–RCP8.5), with a projected decline of −1.13 p.p. (95% CI −2.00 to −0.38 p.p.) by mid-century and a staggering −4.50 p.p. (95% CI −9.44 to −1.63 p.p.) decrease by 2100. Similar but shallower declines are projected in central Africa, where end-of-century reductions could reach between −0.09 p.p. (SSP1–RCP2.6; 95% CI −0.51 to 0.27 p.p.) and −1.56 p.p. (SSP5–RCP8.5; 95% CI −4.72 to 0.48 p.p.). Conversely, localized increases in prevalence will continue in the cooler parts of the Ethiopian highlands, the greater Rift Valley region and coastal southern Africa, potentially reaching 5 p.p. or more in some areas. The overall effect across East and southern Africa is a projected increase in prevalence, except in the highest emissions scenario (SSP5–RCP8.5), where both regions start to experience declines by mid-century, with East Africa eventually falling −0.68 p.p. (95% CI −3.00 to 1.10 p.p.) below present-day levels by 2100.Broadly, our results suggest that the main effect of climate change mitigation will be to keep average temperatures in sub-Saharan Africa closer to the optimum range for malaria transmission. However, for many cooler localities, such as in parts of East and southern Africa, greenhouse gas emission reductions may prevent substantial climate change-driven increases in malaria prevalence, although uncertainty is high. For example, by mid-century, limiting global warming to below the +2 °C limit in the Paris Agreement (achieved under SSP1–RCP2.6) is projected to prevent an estimated 2 cases of malaria per 1,000 children in southern Africa (95% CI −2 to 6) compared with an intermediate-emissions scenario (SSP2–RCP4.5 +2.0 °C in 2048–2052; +3.0 °C in 2096–2100). By the end of the century, these benefits could be even greater, with 5 excess cases averted per 1,000 children in southern Africa (95% CI −4 to 14) and in high-elevation (more than 1 km) East Africa (95% CI −3 to 13; Extended Data Table 2 and Extended Data Fig. 10).DiscussionIn this study, we applied an end-to-end impact attribution framework to a century of malaria surveillance, allowing us to estimate the historical and projected future effect of anthropogenic climate change on childhood malaria in sub-Saharan Africa. We found a 59% likelihood that anthropogenic climate change since 1901 has increased malaria burden; on average across Africa, a small and uncertain number of excess malaria cases (mean of 1 per 1,000 children with 95% CI of −4 to 6) can be attributed to historical human-caused climate change. However, this burden falls disproportionately on southern and East Africa; we estimated a 91% and 84% likelihood, respectively, that anthropogenic climate change has increased present-day malaria prevalence in these regions. We have projected that prevalence in both southern and East Africa will remain elevated in the future: even in a low-emissions scenario likely to limit global warming below +2 °C (SSP1–RCP2.6), we have estimated that these regions will face 3 (95% CI −2 to 10) and 1 (95% CI −4 to 5) excess cases of malaria per 1,000 children by 2100 compared to the present day, respectively. By contrast, across many other regions of Africa, we have projected that the overall impact of future climate change will be a net reduction in malaria: these changes are projected to be most dramatic in West and central Africa, where future climate change could reduce prevalence by up to 45 (West Africa; 95% CI −94 to −16) and 16 (central Africa; 95% CI −47 to 5) cases per 1,000 children in a high-emissions scenario (SSP5–RCP8.5). Our results suggest that climate change could be synergistic with eradication efforts in countries such as Nigeria and the Democratic Republic of the Congo, where the present-day burden of malaria is highest, but will continue to create new risks in countries such as Ethiopia and South Africa.Spanning multiple centuries, our analysis is the most comprehensive look to date at the effect of climate change on any infectious disease, and brings new clarity to a decades-long debate in malaria research. Whereas some work has questioned the plausibility that overall declines in continent-wide prevalence would conceal a climate-linked increase2,15, the 0.074 p.p. increase in PfPR2−10 that we attributed to historical anthropogenic climate change could easily be masked by the more than 200-fold greater overall reduction observed across sub-Saharan Africa over the same period2. Our regional estimates also generally align with previous laboratory-based or site-specific empirical work, which suggests that East and southern Africa are experiencing shifts towards temperatures that are newly permissive to transmission or have longer malaria seasons6, whereas in West and central Africa, climate change impacts have been harder to detect, and future warming might exceed the physiological limits of malaria transmission6,9. Of note, our study does provide robust, empirical evidence that human-caused climate change has at least marginally contributed to malaria resurgence in high-altitude Kenya and Ethiopia, consistent with local epidemic time series and simulated dynamics based on local weather station data17,20,29.Our study therefore reconciles three long-standing ideas that are sometimes treated as paradoxical: anthropogenic climate change is not the primary force shaping past, or probably future, trends in malaria prevalence2,12,15. However, it is more likely than not that anthropogenic climate change has increased the burden of malaria in sub-Saharan Africa10,11,17,20, and at high elevations and latitudes, will continue to for several more decades6. Nevertheless, rising temperatures at lower latitudes and elevations in Africa will mostly align with future efforts to eradicate P. falciparum from sub-Saharan Africa6,15,52. Future work will be needed to situate these global trends in local contexts, particularly through work that leverages longitudinal data from malaria-endemic communities. Similarly, our study provides a long view of future climate change impacts under different emissions scenarios, but cannot be used as a forecast of year-to-year variation in malaria transmission. Future work should explore emerging methods for near-term climate prediction, which could give public health decision-makers information about what to expect over the next year53 to decade54, the timescale most relevant to malaria control.In spite of climate change, elimination campaigns have already achieved substantial reductions in malaria endemicity over the past century. Our study underscores that the combined benefits of disease surveillance, healthcare, vector control and economic development can easily counter-balance climate change impacts in most places, and that malaria elimination within the next generation remains plausible, even in the face of climate change. Another recent study has found that passive changes in climate, land use and development will lead to modest reductions in malaria prevalence over the next 25 years, but with 80% effective coverage of chemotherapy, indoor residual spraying and insecticide-treated nets, malaria could be nearly eliminated in sub-Saharan Africa by 2050 (ref. 55). In the past few years, the odds of success have become substantially higher thanks to the new RTS,S and R21 malaria vaccines: a four-dose R21 schedule could prevent between one-third and one-half of all malaria cases in children under 5 years of age56. Even in places where climate change is increasing malaria transmission, the combined use of classic and new interventions should have a much greater effect, provided that these interventions are able to continue.At the time of writing, progress towards malaria elimination hangs in the balance, as global health financing faces an unprecedented moment of resource scarcity. Several recent anecdotes have raised relevant concerns about the fragility of elimination, such as the resurgence of malaria in Ecuador and Peru associated with migration from Venezuela57, or the estimated 10,000 excess deaths due to malaria—and 3.5 million untreated cases—caused by healthcare disruptions during the 2014 Ebola virus epidemic in West Africa58. Concerns about climate-linked resurgence are also more credible given the ongoing invasion of the Anopheles stephensi mosquito, which thrives in cities, has already been reported in several locations in East Africa and may be able to transmit P. falciparum up to much higher temperatures (approximately 37 °C) than A. gambiae can (approximately 30 °C)5. Although our data provide suggestive evidence that temperature has historically had a smaller effect on prevalence in urban areas than in rural areas (Extended Data Fig. 5), if A. stephensi were to become a dominant vector across the continent, climate change might become an even more pressing concern59,60. These risks only add more urgency to the global goals of eliminating both malaria and greenhouse gas emissions.MethodsMalaria prevalence dataWe used a recently published database of P. falciparum prevalence in sub-Saharan Africa2. This compendium, compiled by Snow et al. over more than two decades, is one of the most spatially and temporally complete publicly available databases of infectious disease burden. The database covers the period from 1900 to 2016, although sampling has increased substantially since the turn of the century (pre-2000: n = 32,533; post-2000: n = 17,892). Most prevalence surveys used microscopy for diagnostics (n = 36,805) but a substantial portion of data also derive from rapid diagnostic tests (n = 11,154). The data have been compiled from a mix of archival research through public health documents, including the records of colonial governments and elimination campaigns from different periods; national survey data; electronic records published in peer-reviewed journals and grey data sources (for example, World Health Organization technical documents); and a mix of other sources compiled by international organizations. Records were georeferenced in the original study using a standard set of protocols, with a 5-km grid uncertainty threshold for point data, and broader areas stored as administrative polygons. In total, the data include a total of 50,425 prevalence surveys at a total of 36,966 unique georeferenced locations.The Snow et al. data cover all available prevalence surveys, including all age ranges, but were converted by the authors of the original study to a standardized estimate of prevalence in children 2–10 years of age (PfPR2−10), using a catalytic conversion Muench model. We chose to use these standardized estimates of childhood malaria prevalence because falciparum malaria has the highest mortality in children and pregnant women. The trends that we infer should generally be representative of broader transmission across age groups. In some cases, we note that declines in early-life exposure can lead to increases in incidence in adults61; however, these impacts are likely to be small, particularly given that active and passive improvements in malaria prevention, control and treatment much more directly determine trends in adult malaria risk.Climate dataWe used two sets of climate data in this study. The first is an observational dataset from the Climatic Research Unit (CRU-TS; v4.03 for model training and bias correction), which is constructed from monthly observations from extensive networks of meteorological stations from around the globe62. CRU-TS provides land-only climatic variables at a spatial resolution of 0.5° × 0.5° extending from 1901 to present (although our analysis is limited to the period 1901–2016). The second set of data is from ten global climate models (GCMs) selected from the sixth phase of the Coupled Model Intercomparison Project (CMIP6): ACCESS-CM2, ACCESS-ESM1-5, BCC-CSM2-MR, CanESM5, FGOALS-g3, GFDL-ESM4, IPSL-CM6A-LR, MIROC6, MRI-ESM2-0 and NorESM2-LM. In our historical analysis, we analysed (per GCM) one model realization of the ‘historical’ simulation, which includes anthropogenic greenhouse gas emissions, and one realization from the ‘historical-natural’ simulation, which includes only solar and volcanic climate forcing. For both the historical and historical-natural (hereafter and in the main text, ‘historical climate’ and ‘historical counterfactual’, respectively) simulations, we analysed the period 1901–2014.To investigate the continued effect of climate change on malaria prevalence between 2015 and 2100, we analysed three CMIP6 future climate change simulations from each of the 10 GCMs. SSPs refer to the level of potential future global development (social, economic and technological) and the implication for climate change mitigation and/or adaptation actions or policy63,64. SSPs are combined with various possible future radiative forcings (RCPs) to form the climate change scenarios used in CMIP6. Of the available SSP–RCP scenarios, we selected and used three. The first two suggest enhanced human development outcomes with increased potential towards a more sustainable (SSP1)65 or a less sustainable (SSP5)66 economy. The third, SSP2 (ref. 67), is a mid-way scenario, which assumes a future that mostly follows historical trends64. We selected these scenarios in combination with a low (SSP1–RCP2.6), intermediate (SSP2–RCP4.5) and high (SSP5–RCP8.5) greenhouse gas concentration scenario.We applied a standard quantile–quantile (Q–Q) bias-correction68,69 to the CMIP6 precipitation and temperature datasets for both of the historical simulations for the period 1901–2014, and all three future simulations for the period 2015–2100. Before the bias correction, we first remapped all simulated CMIP6 precipitation and temperature datasets to the same grid cell size (0.5° × 0.5°) as the CRU-TS observation data. We then performed for each CMIP6 model, the Q–Q bias correction at each grid point by mapping the quantile values (qi) for the empirical cumulative distribution functions for each of the 12 months over the period 1901–2014 (for each grid point) onto the corresponding quantiles in the observational dataset (CRU-TS), so that the observed precipitation or temperature values associated with qi become the bias-corrected value in the simulations. For the counterfactual (and future) simulations, we first determined, at each grid point, for each value of precipitation or temperature (for each month) over the period 1901–2014 (2015–2100), the equivalent quantile (qj) in the factual simulation and then identified the precipitation or temperature value associated with qj in the observational dataset as the bias-corrected value. We detrended both precipitation and temperature datasets before applying the bias-correction procedure, and then added the trends back after69.Spatial data aggregationOur statistical analysis is designed to isolate variation in the weather that is uncorrelated with other socioeconomic and/or environmental factors that influence malaria prevalence. As detailed in the next section, we build on a large body of climate econometrics research38,40,70 to do so, estimating a model that leverages variation over time in weather conditions within the same location. To estimate such a model, we required observations of malaria prevalence covering the same region in multiple time periods. By contrast, the raw prevalence data that we obtained from ref. 2 are point data observations from individual surveys conducted at different times, such that single geolocations are not observed repeatedly over time. Therefore, we aggregated the point-level data from ref. 2 by averaging PfPR2−10 observations to the first administrative level within each country (that is, state or province level, or as shorthand, ADM1), using shapefiles provided by the Database of Global Administrative Areas dataset v3.6 (www.gadm.org). This level of aggregation provides sufficient granularity to capture differences in climate impacts within countries and to control for local heterogeneity in confounders, while ensuring sufficient data coverage within these units. This aggregation scale has also been conducted in previous work that models this dataset at the same spatial resolution2. For robustness, we also show results from a statistical model that does not aggregate data, and instead uses the prevalence data at its native resolution (see below for details).To compute average prevalence values at the scale of ADM1, we used an unweighted arithmetic mean over all prevalence surveys observed in the corresponding ADM1 month. This approach imposes minimal assumptions on the spatiotemporal process of malaria transmission and requires no additional high-resolution data (for example, population) for use as weights, which are unavailable for sub-Saharan Africa for years as early as 1901. Although previous work aiming to construct comprehensive high-resolution estimates of health outcomes using point data often uses spatiotemporal smoothing methods (for example, ref. 71), doing so here would artificially introduce spatial and temporal correlations that could bias recovered regression coefficients and threaten inference72.We similarly aggregate monthly 0.5° grid-level weather data (from all CRU-TS and CMIP6 models) to the ADM1-month level. To do so without introducing aggregation biases, we applied methods from previous research demonstrating that it is possible to statistically recover nonlinear relationships that take place at high spatial and temporal resolution, even when the resolution of available outcome data is relatively coarse (that is, ADM1-month-level average malaria prevalence)40,73,74,75,76. In our setting, this is achieved by computing nonlinear polynomial transformations of temperature at the grid-cell-by-month level before aggregating these values across administrative units. Such an approach ensures the temperature variables used for estimation reflect the full distribution of temperatures experienced across administrative regions of varying sizes and terrains. For example, many of the 12 ADM1 regions in Ethiopia include both hot low-elevation zones and cold highlands, such that temperatures can vary substantially within an ADM1 during the same month. We computed second-order polynomials at each grid cell before aggregating across such diverse landscapes to ensure the regressor variables capture both extreme cold and extreme heat, even when they occur simultaneously within the boundaries of ADM1.To see this method in practice, let PfPRgit denote average malaria prevalence in children 2–10 years of age in grid cell g located within administrative unit i during month t and let Tgit indicate temperature observed at the same spatiotemporal scale. Following previous studies recovering local-level quadratic responses between malaria prevalence and temperature77,78, we assumed that prevalence in grid cell g in month t is a quadratic function of the temperature experienced in that same grid cell and month (noting that we show results relaxing this assumption, such as other nonlinear functional forms and the possibility of temporal lags): $$Pf{{\rm{P}}{\rm{R}}}_{git}={\beta }_{1}{T}_{git}+{\beta }_{2}{T}_{git}^{2},$$